---
title: "Multi-Step Reasoning with Large Language Models, a Survey"
description: "The authors collected recent papers, mostly from the two years before writing, that build on… 198 references, cited by 56 papers here."
authors: ["Aske Plaat", "Annie Wong", "Suzan Verberne", "Joost Broekens", "Niki van Stein", "Thomas Back"]
year: 2024
venue: "arXiv:2407.11511"
source: "https://arxiv.org/abs/2407.11511"
doi: "10.48550/arxiv.2407.11511"
arxiv: "2407.11511"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/"
---
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# Multi-Step Reasoning with Large Language Models, a Survey

**Authors:** Aske Plaat, Annie Wong, [Suzan Verberne](https://md.research.elamur.ai/free/en/authors/suzan-verberne/), Joost Broekens, Niki van Stein and Thomas Back · **Year:** 2024 · **Venue:** [arXiv:2407.11511](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/) ([ACM Computing Surveys 2024](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/2024/)) · **Publisher:** [Association for Computing Machinery](https://md.research.elamur.ai/free/en/publishers/association-for-computing-machinery/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2407.11511) · [PDF](https://arxiv.org/pdf/2407.11511) · [DOI 10.48550/arxiv.2407.11511](https://doi.org/10.48550/arxiv.2407.11511) · [arXiv 2407.11511](https://arxiv.org/abs/2407.11511) · [OpenAlex](https://openalex.org/W4415915064)

**Citations:** 50 citations (OpenAlex, 2026-10-14) · [56](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models handle text generation well through prompting, but they long performed poorly on problems that require several chained inferences, such as school-level arithmetic word tasks. This survey from Leiden University reviews the research that grew out of Chain-of-thought prompting, in which the model is pushed to write out intermediate steps before giving its answer. The authors collected recent papers, mostly from the two years before writing, that build on Chain-of-thought and report results on reasoning benchmarks. These start with GSM8K and extend to code, games, puzzles, robotics and web navigation. The authors sort methods with a three-part taxonomy: how steps are produced, how they are checked, and how the overall reasoning process is steered, for example by external search or reinforcement learning. They conclude that such techniques now reach well beyond arithmetic. Translating problems into executable code helps limit error accumulation, and reinforcement learning increasingly connects inference-time reasoning with finetuning. The paper ends with a research agenda, including reasoning in languages other than English, which is directly relevant to Ukrainian-language work.

## Key points
- GSM8K, released in 2021 with about 8,500 grade-school math word problems, is the most widely used benchmark in the surveyed work. With older models such as GPT-3, multi-step prompting gained roughly 20–50 percentage points over plain prompting.
- The taxonomy splits approaches into step generation, step evaluation and control of the reasoning steps. Control is often implemented as a Python wrapper that generates prompts and is driven by search or reinforcement learning.
- Much of the progress in OpenAI o1/o3 and DeepSeek R1 is attributed to these methods. RLVR and GRPO reuse inference-time reasoning outputs to finetune models for reasoning.
- Reported open problems include unfaithful reasoning, where a correct answer comes from a flawed chain, hallucination, bias, and self-reflection that is itself unreliable. Self-verification and RAG partly mitigate these, and reasoning skills can often be distilled into small models.
- Agenda items include combining symbolic and connectionist methods, multimodal grounded world models, and value-aligned reasoning. The authors also call for reasoning benchmarks beyond English, noting that existing multilingual ones are mostly QA and NLI and that low-resource languages remain unaddressed.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Leiden University](https://md.research.elamur.ai/free/en/universities/leiden-university/) — University, Leiden, Netherlands · 37 works · 1,408 citations

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🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works

## Cite

Cite as: Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein and Thomas Back. 2024. Multi-Step Reasoning with Large Language Models, a Survey. arXiv:2407.11511.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{plaat2024multi,
  title = {{Multi-Step Reasoning with Large Language Models, a Survey}},
  author = {Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki {van Stein} and Thomas Back},
  year = {2024},
  doi = {10.48550/arxiv.2407.11511},
  eprint = {2407.11511},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2407.11511}
}
```

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## References

198 references, 99 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Janice Ahn, Rishu Verma, Renze Lou, Di Liu, Rui Zhang, and Wenpeng Yin. [Large language models for mathematical reasoning: Progresses and challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/). In Association for Computational Linguistics, arXiv preprint arXiv:2402.00157, 2024.
2. Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. [Do as i can, not as i say: Grounding language in robotic affordances](https://md.research.elamur.ai/free/en/papers/ahn-2022-i-i-say-grounding-language/). In Conference on Robot Learning, 2022.
3. Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. [Mathqa: Towards interpretable math word problem solving with operation-based formalisms](https://md.research.elamur.ai/free/en/papers/amini-2019-mathqa-interpretable-math-word-problem/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguis…
4. Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, and Suzan Verberne. Solid: Self-seeding and multi-intent self-instructing llms for generating intent-aware information-seeking dialogs. In Association for Computational Linguistics, arXiv preprint arX… [find](https://scholar.google.com/scholar?q=Arian+Askari%2C+Roxana+Petcu%2C+Chuan+Meng%2C+Mohammad+Aliannejadi%2C+Amin+Abolghasemi%2C+Evangelos+Kanoulas%2C+and+Suzan+Verberne%2E+Solid%3A+Self%2Dseeding+and+multi%2Dintent+self%2Dinstructing+llms+for+generating+intent)
5. Gasper Begus, Maksymilian Dabkowski, and Ryan Rhodes. Large linguistic models: Investigating llms’ metalinguistic abilities. IEEE Transactions on Artificial Intelligence, 2025. [find](https://scholar.google.com/scholar?q=Gasper+Begus%2C+Maksymilian+Dabkowski%2C+and+Ryan+Rhodes%2E+Large+linguistic+models%3A+Investigating+llms%E2%80%99+metalinguistic+abilities%2E+IEEE+Transactions+on+Artificial+Intelligence%2C+2025%2E)
6. Richard Bellman. Dynamic programming. Science, 153(3731):34–37, 1966. [find](https://scholar.google.com/scholar?q=Richard+Bellman%2E+Dynamic+programming%2E+Science%2C+153%283731%29%3A34%E2%80%9337%2C+1966%2E)
7. Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 610–623, 2021. [find](https://scholar.google.com/scholar?q=Emily+M+Bender%2C+Timnit+Gebru%2C+Angelina+McMillan%2DMajor%2C+and+Shmargaret+Shmitchell%2E+On+the+dangers+of+stochastic+parrots%3A+Can+language+models+be+too+big%3F+In+Proceedings+of+the+2021+ACM+conference+on+fai)
8. Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. [Curriculum learning](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/). In Proceedings of the 26th annual international conference on machine learning, pages 41–48, 2009.
9. Leonard Bereska and Efstratios Gavves. Mechanistic interpretability for ai safety–a review. Transactions on Machine Learning Research, arXiv preprint arXiv:2404.[14082, 2024](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/).
10. Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans. The reversal curse: Llms trained on a is b fail to learn b is a. In International Conference on Learning Representations, 2024. [find](https://scholar.google.com/scholar?q=Lukas+Berglund%2C+Meg+Tong%2C+Max+Kaufmann%2C+Mikita+Balesni%2C+Asa+Cooper+Stickland%2C+Tomasz+Korbak%2C+and+Owain+Evans%2E+The+reversal+curse%3A+Llms+trained+on+a+is+b+fail+to+learn+b+is+a%2E+In+International+Conferen)
11. Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI Conference on Artificial Int… [find](https://scholar.google.com/scholar?q=Maciej+Besta%2C+Nils+Blach%2C+Ales+Kubicek%2C+Robert+Gerstenberger%2C+Michal+Podstawski%2C+Lukas+Gianinazzi%2C+Joanna+Gajda%2C+Tomasz+Lehmann%2C+Hubert+Niewiadomski%2C+Piotr+Nyczyk%2C+et+al%2E+Graph+of+thoughts%3A+Solving+el)
12. Bernd Bohnet, Azade Nova, Aaron T Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, and Hanie Sedghi. Exploring and benchmarking the planning capabilities of large language models. arXiv preprint arXiv:2406.13094, 2024. — [arXiv:2406.13094](https://arxiv.org/abs/2406.13094) [find](https://scholar.google.com/scholar?q=Bernd+Bohnet%2C+Azade+Nova%2C+Aaron+T+Parisi%2C+Kevin+Swersky%2C+Katayoon+Goshvadi%2C+Hanjun+Dai%2C+Dale+Schuurmans%2C+Noah+Fiedel%2C+and+Hanie+Sedghi%2E+Exploring+and+benchmarking+the+planning+capabilities+of+large+la)
13. Leo Breiman. Random forests. Machine learning, 45:5–32, 2001. [find](https://scholar.google.com/scholar?q=Leo+Breiman%2E+Random+forests%2E+Machine+learning%2C+45%3A5%E2%80%9332%2C+2001%2E)
14. Joost Broekens, Bernhard Hilpert, Suzan Verberne, Kim Baraka, Patrick Gebhard, and Aske Plaat. Fine-grained affective processing capabilities emerging from large language models. In 2023 11th Intl Conf on Affective Computing and Intelligent Interaction (ACII), pages 1–8. IEEE, 2023. [find](https://scholar.google.com/scholar?q=Joost+Broekens%2C+Bernhard+Hilpert%2C+Suzan+Verberne%2C+Kim+Baraka%2C+Patrick+Gebhard%2C+and+Aske+Plaat%2E+Fine%2Dgrained+affective+processing+capabilities+emerging+from+large+language+models%2E+In+2023+11th+Intl+Con)
15. Rodney A Brooks. Elephants don’t play chess. Robotics and autonomous systems, 6(1-2):3–15, 1990. [find](https://scholar.google.com/scholar?q=Rodney+A+Brooks%2E+Elephants+don%E2%80%99t+play+chess%2E+Robotics+and+autonomous+systems%2C+6%281%2D2%29%3A3%E2%80%9315%2C+1990%2E)
16. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33:1877–1901, 2020.
17. Patrick Bryant, Gabriele Pozzati, and Arne Elofsson. Improved prediction of protein-protein interactions using alphafold2. Nature communications, 13(1):1265, 2022. [find](https://scholar.google.com/scholar?q=Patrick+Bryant%2C+Gabriele+Pozzati%2C+and+Arne+Elofsson%2E+Improved+prediction+of+protein%2Dprotein+interactions+using+alphafold2%2E+Nature+communications%2C+13%281%29%3A1265%2C+2022%2E)
18. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. [Evaluating large language models trained on code](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/). arXiv preprint arXiv:2107.03374, 2021.
19. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. Transactions of Machine Learning Research; arXiv preprint arXiv:2211.12588, 2023. [find](https://scholar.google.com/scholar?q=Wenhu+Chen%2C+Xueguang+Ma%2C+Xinyi+Wang%2C+and+William+W+Cohen%2E+Program+of+thoughts+prompting%3A+Disentangling+computation+from+reasoning+for+numerical+reasoning+tasks%2E+Transactions+of+Machine+Learning+Resear)
20. Xi Chen, Aske Plaat, and Niki van Stein. How does chain of thought think? mechanistic interpretability of chain-of-thought reasoning with sparse autoencoding. arXiv preprint arXiv:2507.22928, 2025. — [arXiv:2507.22928](https://arxiv.org/abs/2507.22928) [find](https://scholar.google.com/scholar?q=Xi+Chen%2C+Aske+Plaat%2C+and+Niki+van+Stein%2E+How+does+chain+of+thought+think%3F+mechanistic+interpretability+of+chain%2Dof%2Dthought+reasoning+with+sparse+autoencoding%2E+arXiv+preprint+arXiv%3A2507%2E22928%2C+2025%2E+%E2%80%94+)
21. Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, and Quoc V Le. Neural symbolic reader: Scalable integration of distributed and symbolic representations for reading comprehension. In International Conference on Learning Representations, 2019. [find](https://scholar.google.com/scholar?q=Xinyun+Chen%2C+Chen+Liang%2C+Adams+Wei+Yu%2C+Denny+Zhou%2C+Dawn+Song%2C+and+Quoc+V+Le%2E+Neural+symbolic+reader%3A+Scalable+integration+of+distributed+and+symbolic+representations+for+reading+comprehension%2E+In+Inte)
22. Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug. In The Twelfth International Conference on Learning Representations, 2024. URL [https://openreview.net/forum?id=KuPixIqPiq](https://openreview.net/forum?id=KuPixIqPiq). [↗](https://openreview.net/forum?id=KuPixIqPiq)
23. Ting-Rui Chiang and Yun-Nung Chen. Semantically-aligned equation generation for solving and reasoning math word problems. In Association for Computational Linguistics, 2019. [find](https://scholar.google.com/scholar?q=Ting%2DRui+Chiang+and+Yun%2DNung+Chen%2E+Semantically%2Daligned+equation+generation+for+solving+and+reasoning+math+word+problems%2E+In+Association+for+Computational+Linguistics%2C+2019%2E)
24. Wonje Choi, Woo Kyung Kim, Minjong Yoo, and Honguk Woo. Embodied cot distillation from llm to off-the-shelf agents. In Forty-first International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Wonje+Choi%2C+Woo+Kyung+Kim%2C+Minjong+Yoo%2C+and+Honguk+Woo%2E+Embodied+cot+distillation+from+llm+to+off%2Dthe%2Dshelf+agents%2E+In+Forty%2Dfirst+International+Conference+on+Machine+Learning%2E)
25. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. [Palm: Scaling language modeling with pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/). Journal of Machine Learning Research, 24(240):1–113, 2023. Manuscript submitted to ACM
26. Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V Le, Sergey Levine, and Yi Ma. [Sft memorizes, rl generalizes: A comparative study of foundation model post-training](https://md.research.elamur.ai/free/en/papers/chu-2025-sft-memorizes-rl-generalizes-comparative/). arXiv preprint arXiv:2501.17161, 2025.
27. Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, and Ting Liu. A survey of chain of thought reasoning: Advances, frontiers and future. In Association for Computational Linguistics, 2024. [find](https://scholar.google.com/scholar?q=Zheng+Chu%2C+Jingchang+Chen%2C+Qianglong+Chen%2C+Weijiang+Yu%2C+Tao+He%2C+Haotian+Wang%2C+Weihua+Peng%2C+Ming+Liu%2C+Bing+Qin%2C+and+Ting+Liu%2E+A+survey+of+chain+of+thought+reasoning%3A+Advances%2C+frontiers+and+future%2E+In+)
28. Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, and Ting Liu. [Navigate through enigmatic labyrinth a survey of chain of thought reasoning: Advances, frontiers and future](https://md.research.elamur.ai/free/en/papers/chu-2023-navigate-through-enigmatic-labyrinth-survey/). In Proceedings of the 62nd Annual Meeting of the Association for C…
29. Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Fan Yang, Mengnan Du, Xuanting Cai, and Xia Hu. [Faithlm: Towards faithful explanations for large language models](https://md.research.elamur.ai/free/en/papers/chuang-2026-faithlm-faithful-explanations-large-language/). arXiv preprint arXiv:2402.04678, 2024.
30. Nathan Cloos, Meagan Jens, Michelangelo Naim, Yen-Ling Kuo, Ignacio Cases, Andrei Barbu, and Christopher J Cueva. Baba is ai: Break the rules to beat the benchmark. arXiv preprint arXiv:2407.13729, 2024. — [arXiv:2407.13729](https://arxiv.org/abs/2407.13729) [find](https://scholar.google.com/scholar?q=Nathan+Cloos%2C+Meagan+Jens%2C+Michelangelo+Naim%2C+Yen%2DLing+Kuo%2C+Ignacio+Cases%2C+Andrei+Barbu%2C+and+Christopher+J+Cueva%2E+Baba+is+ai%3A+Break+the+rules+to+beat+the+benchmark%2E+arXiv+preprint+arXiv%3A2407%2E13729%2C+20)
31. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. [Training verifiers to solve math word problems](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/). arXiv preprint arXiv:2110.14168, 2021.
32. MAA Codeforces. American invitational mathematics examination-aime 2024, 2024. [find](https://scholar.google.com/scholar?q=MAA+Codeforces%2E+American+invitational+mathematics+examination%2Daime+2024%2C+2024%2E)
33. Corinna Cortes and Vladimir Vapnik. [Support-vector networks](https://md.research.elamur.ai/free/en/papers/cortes-1995-support-vector-networks/). Machine learning, 20:273–297, 1995.
34. Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B Hall, and Ming-Wei Chang. [Promptagator: Few-shot dense retrieval from 8 examples](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/). In International Conference on Learning Representations, 2023.
35. Can Demircan, Tankred Saanum, Akshay K Jagadish, Marcel Binz, and Eric Schulz. Sparse autoencoders reveal temporal difference learning in large language models. arXiv preprint arXiv:2410.01280, 2024. — [arXiv:2410.01280](https://arxiv.org/abs/2410.01280) [find](https://scholar.google.com/scholar?q=Can+Demircan%2C+Tankred+Saanum%2C+Akshay+K+Jagadish%2C+Marcel+Binz%2C+and+Eric+Schulz%2E+Sparse+autoencoders+reveal+temporal+difference+learning+in+large+language+models%2E+arXiv+preprint+arXiv%3A2410%2E01280%2C+2024%2E+)
36. Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy Lillicrap, Danilo Rezende, Yoshua Bengio, Michael Mozer, and Sanjeev Arora. Metacognitive capabilities of llms: An exploration in mathematical problem solving. arXiv preprint arXiv:2405.12205, 2024. [find](https://scholar.google.com/scholar?q=Aniket+Didolkar%2C+Anirudh+Goyal%2C+Nan+Rosemary+Ke%2C+Siyuan+Guo%2C+Michal+Valko%2C+Timothy+Lillicrap%2C+Danilo+Rezende%2C+Yoshua+Bengio%2C+Michael+Mozer%2C+and+Sanjeev+Arora%2E+Metacognitive+capabilities+of+llms%3A+An+ex)
37. Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. [A survey on in-context learning](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/). In Association for Computational Linguistics, 2023.
38. Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. [Kimi k1.5: Scaling reinforcement learning with llms](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/). arXiv preprint arXiv:2501.12599, 2025.
39. Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. [Diversity is all you need: Learning skills without a reward function](https://md.research.elamur.ai/free/en/papers/eysenbach-2018-diversity-all-need-learning-skills/). In International Conference on Learning Representations, 2019.
40. Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, et al. [Minedojo: Building open-ended embodied agents with internet-scale knowledge](https://md.research.elamur.ai/free/en/papers/fan-2022-minedojo-building-open-ended-embodied/). Advances in Neural Information Processing Systems, 35:18343–18362, 2022.
41. Richard E Fikes and Nils J Nilsson. [Strips: A new approach to the application of theorem proving to problem solving](https://md.research.elamur.ai/free/en/papers/fikes-1971-strips-new-approach-application-theorem/). Artificial intelligence, 2(3-4): 189–208, 1971.
42. Chelsea Finn, Pieter Abbeel, and Sergey Levine. [Model-agnostic meta-learning for fast adaptation of deep networks](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/). In International conference on machine learning, pages 1126–1135. PMLR, 2017.
43. Peter Flach. Machine learning: the art and science of algorithms that make sense of data. Cambridge university press, 2012. [find](https://scholar.google.com/scholar?q=Peter+Flach%2E+Machine+learning%3A+the+art+and+science+of+algorithms+that+make+sense+of+data%2E+Cambridge+university+press%2C+2012%2E)
44. Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. [Complexity-based prompting for multi-step reasoning](https://md.research.elamur.ai/free/en/papers/fu-2022-complexity-based-prompting-multi-step/). In The Eleventh International Conference on Learning Representations, 2022.
45. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models. In International Conference on Machine Learning, pages 10764–10799. PMLR, 2023. [find](https://scholar.google.com/scholar?q=Luyu+Gao%2C+Aman+Madaan%2C+Shuyan+Zhou%2C+Uri+Alon%2C+Pengfei+Liu%2C+Yiming+Yang%2C+Jamie+Callan%2C+and+Graham+Neubig%2E+Pal%3A+Program%2Daided+language+models%2E+In+International+Conference+on+Machine+Learning%2C+pages+1076)
46. Peizhong Gao, Ao Xie, Shaoguang Mao, Wenshan Wu, Yan Xia, Haipeng Mi, and Furu Wei. Meta reasoning for large language models. arXiv preprint arXiv:2406.11698, 2024. — [arXiv:2406.11698](https://arxiv.org/abs/2406.11698) [find](https://scholar.google.com/scholar?q=Peizhong+Gao%2C+Ao+Xie%2C+Shaoguang+Mao%2C+Wenshan+Wu%2C+Yan+Xia%2C+Haipeng+Mi%2C+and+Furu+Wei%2E+Meta+reasoning+for+large+language+models%2E+arXiv+preprint+arXiv%3A2406%2E11698%2C+2024%2E+%E2%80%94+%5BarXiv%3A2406%2E11698%5D%28https%3A%2F%2Farxiv%2E)
47. Louie Giray. Prompt engineering with chatgpt: a guide for academic writers. Annals of biomedical engineering, 51(12):2629–2633, 2023. [find](https://scholar.google.com/scholar?q=Louie+Giray%2E+Prompt+engineering+with+chatgpt%3A+a+guide+for+academic+writers%2E+Annals+of+biomedical+engineering%2C+51%2812%29%3A2629%E2%80%932633%2C+2023%2E)
48. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. [find](https://scholar.google.com/scholar?q=Ian+Goodfellow%2C+Yoshua+Bengio%2C+and+Aaron+Courville%2E+Deep+learning%2E+MIT+press%2C+2016%2E)
49. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. Minillm: Knowledge distillation of large language models. In The Twelfth International Conference on Learning Representations, 2023. [find](https://scholar.google.com/scholar?q=Yuxian+Gu%2C+Li+Dong%2C+Furu+Wei%2C+and+Minlie+Huang%2E+Minillm%3A+Knowledge+distillation+of+large+language+models%2E+In+The+Twelfth+International+Conference+on+Learning+Representations%2C+2023%2E)
50. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). arXiv preprint arXiv:2501.12948, 2025.
51. Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, et al. [Folio: Natural language reasoning with first-order logic](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/). arXiv preprint arXiv:2209.00840, 2022.
52. Nikolaus Hansen and Raymond Ros. Black-box optimization benchmarking of newuoa compared to bipop-cma-es: on the bbob noiseless testbed. In Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, pages 1519–1526, 2010. [find](https://scholar.google.com/scholar?q=Nikolaus+Hansen+and+Raymond+Ros%2E+Black%2Dbox+optimization+benchmarking+of+newuoa+compared+to+bipop%2Dcma%2Des%3A+on+the+bbob+noiseless+testbed%2E+In+Proceedings+of+the+12th+annual+conference+companion+on+Geneti)
53. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. [Measuring mathematical problem solving with the math dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/). In Advances in Neural Information Processing Systems, 2021.
54. Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al. [Scaling laws for autoregressive generative modeling](https://md.research.elamur.ai/free/en/papers/henighan-2020-scaling-laws-autoregressive-generative-modeling/). arXiv preprint arXiv:2010.14701, 2020.
55. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. [Training compute-optimal large language models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/). In Advances in Neural Information Processing Systems, 2022. Manuscript sub…
56. Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. Meta-learning in neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(9):5149–5169, 2021. [find](https://scholar.google.com/scholar?q=Timothy+Hospedales%2C+Antreas+Antoniou%2C+Paul+Micaelli%2C+and+Amos+Storkey%2E+Meta%2Dlearning+in+neural+networks%3A+A+survey%2E+IEEE+transactions+on+pattern+analysis+and+machine+intelligence%2C+44%289%29%3A5149%E2%80%935169%2C+2021)
57. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. [Lora: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). In International Conference on Learning Representations, 2022.
58. Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. [Large language models can self-improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/). In Association for Computational Linguistics, 2023.
59. Jie Huang and Kevin Chen-Chuan Chang. [Towards reasoning in large language models: A survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/). In Assoc for Computational Linguistics, 2023.
60. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, et al. [A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/). ACM Transaction on Information Systems, Volume 43, Issue 2; arX…
61. Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al. Inner monologue: Embodied reasoning through planning with language models. In Conference on Robot Learning, 2022. [find](https://scholar.google.com/scholar?q=Wenlong+Huang%2C+Fei+Xia%2C+Ted+Xiao%2C+Harris+Chan%2C+Jacky+Liang%2C+Pete+Florence%2C+Andy+Zeng%2C+Jonathan+Tompson%2C+Igor+Mordatch%2C+Yevgen+Chebotar%2C+et+al%2E+Inner+monologue%3A+Embodied+reasoning+through+planning+with)
62. Zhen Huang, Haoyang Zou, Xuefeng Li, Yixiu Liu, Yuxiang Zheng, Ethan Chern, Shijie Xia, Yiwei Qin, Weizhe Yuan, and Pengfei Liu. O1 replication journey–part 2: Surpassing o1-preview through simple distillation, big progress or bitter lesson? arXiv preprint arXiv:2411.16489, 2024. [find](https://scholar.google.com/scholar?q=Zhen+Huang%2C+Haoyang+Zou%2C+Xuefeng+Li%2C+Yixiu+Liu%2C+Yuxiang+Zheng%2C+Ethan+Chern%2C+Shijie+Xia%2C+Yiwei+Qin%2C+Weizhe+Yuan%2C+and+Pengfei+Liu%2E+O1+replication+journey%E2%80%93part+2%3A+Surpassing+o1%2Dpreview+through+simple+dis)
63. Mike Huisman, Jan N Van Rijn, and Aske Plaat. A survey of deep meta-learning. Artificial Intelligence Review, 54(6):4483–4541, 2021. [find](https://scholar.google.com/scholar?q=Mike+Huisman%2C+Jan+N+Van+Rijn%2C+and+Aske+Plaat%2E+A+survey+of+deep+meta%2Dlearning%2E+Artificial+Intelligence+Review%2C+54%286%29%3A4483%E2%80%934541%2C+2021%2E)
64. Shima Imani, L Du, and H Shrivastava. [Mathprompter: Mathematical reasoning using large language models](https://md.research.elamur.ai/free/en/papers/imani-2023-mathprompter-mathematical-reasoning-large-language/). In Association for Computational Linguistics, 2023.
65. Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Computer vision and pattern recognition, pages 2704–2713, 2018. [find](https://scholar.google.com/scholar?q=Benoit+Jacob%2C+Skirmantas+Kligys%2C+Bo+Chen%2C+Menglong+Zhu%2C+Matthew+Tang%2C+Andrew+Howard%2C+Hartwig+Adam%2C+and+Dmitry+Kalenichenko%2E+Quantization+and+training+of+neural+networks+for+efficient+integer%2Darithmeti)
66. Mingyu Jin, Qinkai Yu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du, et al. [The impact of reasoning step length on large language models](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/). In Association for Computational Linguistics, 2024.
67. John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al. [Highly accurate protein structure prediction with alphafold](https://md.research.elamur.ai/free/en/papers/jumper-2021-highly-accurate-protein-structure-prediction/). nature, 596(7873):583–589, 2021.
68. Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. Reinforcement learning: A survey. Journal of artificial intelligence research, 4: 237–285, 1996. [find](https://scholar.google.com/scholar?q=Leslie+Pack+Kaelbling%2C+Michael+L+Littman%2C+and+Andrew+W+Moore%2E+Reinforcement+learning%3A+A+survey%2E+Journal+of+artificial+intelligence+research%2C+4%3A+237%E2%80%93285%2C+1996%2E)
69. Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Kaya Stechly, Mudit Verma, Siddhant Bhambri, Lucas Saldyt, and Anil Murthy. Llms can’t plan, but can help planning in llm-modulo frameworks. In International Conference on Machine Learning, 2024. — [LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks](https://md.research.elamur.ai/free/en/papers/kambhampati-2024-llms-t-plan-but-help/)
70. Ryo Kamoi, Yusen Zhang, Nan Zhang, Jiawei Han, and Rui Zhang. [When can llms actually correct their own mistakes? a critical survey of self-correction of llms](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/). Transactions of the Association for Computational Linguistics, 12:1417–1440, 2024.
71. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. [Scaling laws for neural language models](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/). arXiv preprint arXiv:2001.08361, 2020.
72. Benjamin Kempinski, Ian Gemp, Kate Larson, Marc Lanctot, Yoram Bachrach, and Tal Kachman. Game of thoughts: Iterative reasoning in game-theoretic domains with large language models. In Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems, pages 1088–1097, 2025. [find](https://scholar.google.com/scholar?q=Benjamin+Kempinski%2C+Ian+Gemp%2C+Kate+Larson%2C+Marc+Lanctot%2C+Yoram+Bachrach%2C+and+Tal+Kachman%2E+Game+of+thoughts%3A+Iterative+reasoning+in+game%2Dtheoretic+domains+with+large+language+models%2E+In+Proc%2E+of+the+24)
73. Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246, 2024. [find](https://scholar.google.com/scholar?q=Moo+Jin+Kim%2C+Karl+Pertsch%2C+Siddharth+Karamcheti%2C+Ted+Xiao%2C+Ashwin+Balakrishna%2C+Suraj+Nair%2C+Rafael+Rafailov%2C+Ethan+Foster%2C+Grace+Lam%2C+Pannag+Sanketi%2C+et+al%2E+Openvla%3A+An+open%2Dsource+vision%2Dlanguage%2Dacti)
74. Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, et al. Findings of the 2022 conference on machine translation (wmt22). In Proceedings of the Seventh Conference on Machine Translation (WMT), pag… [find](https://scholar.google.com/scholar?q=Tom+Kocmi%2C+Rachel+Bawden%2C+Ond%C5%99ej+Bojar%2C+Anton+Dvorkovich%2C+Christian+Federmann%2C+Mark+Fishel%2C+Thamme+Gowda%2C+Yvette+Graham%2C+Roman+Grundkiewicz%2C+Barry+Haddow%2C+et+al%2E+Findings+of+the+2022+conference+on+mac)
75. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/). Advances in neural information processing systems, 35:22199–22213, 2022.
76. Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi. Mawps: A math word problem repository. In 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pages 1152–1157, 2016. [find](https://scholar.google.com/scholar?q=Rik+Koncel%2DKedziorski%2C+Subhro+Roy%2C+Aida+Amini%2C+Nate+Kushman%2C+and+Hannaneh+Hajishirzi%2E+Mawps%3A+A+math+word+problem+repository%2E+In+2016+conference+of+the+north+american+chapter+of+the+association+for+com)
77. Richard E Korf. Algorithms and theory of computation handbook, 22-17, chapter Artificial intelligence search algorithms. Citeseer, 1999. [find](https://scholar.google.com/scholar?q=Richard+E+Korf%2E+Algorithms+and+theory+of+computation+handbook%2C+22%2D17%2C+chapter+Artificial+intelligence+search+algorithms%2E+Citeseer%2C+1999%2E)
78. Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, and Aleksandrs Slivkins. Can large language models explore in-context? arXiv preprint arXiv:2403.15371, 2024. — [arXiv:2403.15371](https://arxiv.org/abs/2403.15371) [find](https://scholar.google.com/scholar?q=Akshay+Krishnamurthy%2C+Keegan+Harris%2C+Dylan+J+Foster%2C+Cyril+Zhang%2C+and+Aleksandrs+Slivkins%2E+Can+large+language+models+explore+in%2Dcontext%3F+arXiv+preprint+arXiv%3A2403%2E15371%2C+2024%2E+%E2%80%94+%5BarXiv%3A2403%2E15371%5D%28htt)
79. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, et al. [Tulu 3: Pushing frontiers in open language model post-training](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/). arXiv preprint arXiv:2411.15124, 2024.
80. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, et al. [Measuring faithfulness in chain-of-thought reasoning](https://md.research.elamur.ai/free/en/papers/lanham-2023-measuring-faithfulness-chain-thought-reasoning/). arXiv preprint arXiv:2307.13702, 2023.
81. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436–444, 2015. [find](https://scholar.google.com/scholar?q=Yann+LeCun%2C+Yoshua+Bengio%2C+and+Geoffrey+Hinton%2E+Deep+learning%2E+Nature%2C+521%287553%29%3A436%E2%80%93444%2C+2015%2E)
82. Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, and Emma Brunskill. Supervised pretraining can learn in-context reinforcement learning. Advances in Neural Information Processing Systems, 36:43057–43083, 2023. [find](https://scholar.google.com/scholar?q=Jonathan+Lee%2C+Annie+Xie%2C+Aldo+Pacchiano%2C+Yash+Chandak%2C+Chelsea+Finn%2C+Ofir+Nachum%2C+and+Emma+Brunskill%2E+Supervised+pretraining+can+learn+in%2Dcontext+reinforcement+learning%2E+Advances+in+Neural+Information)
83. Martha Lewis and Melanie Mitchell. Evaluating the robustness of analogical reasoning in large language models. arXiv preprint arXiv:2411.14215, 2024. Manuscript submitted to ACM — [arXiv:2411.14215](https://arxiv.org/abs/2411.14215) [find](https://scholar.google.com/scholar?q=Martha+Lewis+and+Melanie+Mitchell%2E+Evaluating+the+robustness+of+analogical+reasoning+in+large+language+models%2E+arXiv+preprint+arXiv%3A2411%2E14215%2C+2024%2E+Manuscript+submitted+to+ACM+%E2%80%94+%5BarXiv%3A2411%2E14215%5D%28h)
84. Jindong Li, Yali Fu, Li Fan, Jiahong Liu, Yao Shu, Chengwei Qin, Menglin Yang, Irwin King, and Rex Ying. [Implicit reasoning in large language models: A comprehensive survey](https://md.research.elamur.ai/free/en/papers/li-2025-implicit-reasoning-large-language-models/). arXiv preprint arXiv:2509.02350, 2025.
85. Lei Li, Yongfeng Zhang, and Li Chen. Prompt distillation for efficient llm-based recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 1348–1357, 2023. [find](https://scholar.google.com/scholar?q=Lei+Li%2C+Yongfeng+Zhang%2C+and+Li+Chen%2E+Prompt+distillation+for+efficient+llm%2Dbased+recommendation%2E+In+Proceedings+of+the+32nd+ACM+International+Conference+on+Information+and+Knowledge+Management%2C+pages+)
86. Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, et al. [Explanations from large language models make small reasoners better](https://md.research.elamur.ai/free/en/papers/li-2022-explanations-large-language-models-make/). In Association for the advancement of artificial intelligence, 2024.
87. Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen. [Making language models better reasoners with step-aware verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/). In Association for Computational Linguistics, 2023.
88. Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. Program induction by rationale generation: Learning to solve and explain algebraic word problems. In Association for Computational Linguistics, 2017. [find](https://scholar.google.com/scholar?q=Wang+Ling%2C+Dani+Yogatama%2C+Chris+Dyer%2C+and+Phil+Blunsom%2E+Program+induction+by+rationale+generation%3A+Learning+to+solve+and+explain+algebraic+word+problems%2E+In+Association+for+Computational+Linguistics%2C+)
89. Michael Lederman Littman. Algorithms for sequential decision-making. Brown University, 1996. [find](https://scholar.google.com/scholar?q=Michael+Lederman+Littman%2E+Algorithms+for+sequential+decision%2Dmaking%2E+Brown+University%2C+1996%2E)
90. Fei Liu, Tong Xialiang, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang. Evolution of heuristics: Towards efficient automatic algorithm design using large language model. In Forty-first International Conference on Machine Learning, 2024. [find](https://scholar.google.com/scholar?q=Fei+Liu%2C+Tong+Xialiang%2C+Mingxuan+Yuan%2C+Xi+Lin%2C+Fu+Luo%2C+Zhenkun+Wang%2C+Zhichao+Lu%2C+and+Qingfu+Zhang%2E+Evolution+of+heuristics%3A+Towards+efficient+automatic+algorithm+design+using+large+language+model%2E+In+)
91. Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, Jian Liu, Qiji Zhou, and Yue Zhang. [Evaluating the logical reasoning ability of chatgpt and gpt-4](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/). arXiv preprint arXiv:2304.03439, 2023.
92. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. [Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/). ACM Computing Surveys, 55(9):1–35, 2023.
93. Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. Reasoning on graphs: Faithful and interpretable large language model reasoning. In International Conference on Learning Representations, 2024. [find](https://scholar.google.com/scholar?q=Linhao+Luo%2C+Yuan%2DFang+Li%2C+Gholamreza+Haffari%2C+and+Shirui+Pan%2E+Reasoning+on+graphs%3A+Faithful+and+interpretable+large+language+model+reasoning%2E+In+International+Conference+on+Learning+Representations%2C+2)
94. Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. [Faithful chain-of-thought reasoning](https://md.research.elamur.ai/free/en/papers/lyu-2023-faithful-chain-thought-reasoning/). In Association for Computational Linguistics, 2023.
95. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36, 2023. [find](https://scholar.google.com/scholar?q=Aman+Madaan%2C+Niket+Tandon%2C+Prakhar+Gupta%2C+Skyler+Hallinan%2C+Luyu+Gao%2C+Sarah+Wiegreffe%2C+Uri+Alon%2C+Nouha+Dziri%2C+Shrimai+Prabhumoye%2C+Yiming+Yang%2C+et+al%2E+Self%2Drefine%3A+Iterative+refinement+with+self%2Dfeedbac)
96. Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. [Teaching small language models to reason](https://md.research.elamur.ai/free/en/papers/magister-2023-teaching-small-language-models-reason/). In Association for Computational Linguistics, 2023.
97. Shen-Yun Miao, Chao-Chun Liang, and Keh-Yih Su. A diverse corpus for evaluating and developing english math word problem solvers. In Annual Meeting of the Association for Computational Linguistics, 2020. URL [https://api.semanticscholar.org/CorpusID:220047831](https://api.semanticscholar.org/CorpusID:220047831). [↗](https://api.semanticscholar.org/CorpusID:220047831)
98. Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al. Mixed precision training. In International Conference on Learning Representations, 2018. [find](https://scholar.google.com/scholar?q=Paulius+Micikevicius%2C+Sharan+Narang%2C+Jonah+Alben%2C+Gregory+Diamos%2C+Erich+Elsen%2C+David+Garcia%2C+Boris+Ginsburg%2C+Michael+Houston%2C+Oleksii+Kuchaiev%2C+Ganesh+Venkatesh%2C+et+al%2E+Mixed+precision+training%2E+In+In)
99. Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. [Large language models: A survey](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). arXiv preprint arXiv:2402.06196, 2024.
100. Melanie Mitchell and Douglas R Hofstadter. The emergence of understanding in a computer model of concepts and analogy-making. Physica D: Nonlinear Phenomena, 42(1-3):322–334, 1990. [find](https://scholar.google.com/scholar?q=Melanie+Mitchell+and+Douglas+R+Hofstadter%2E+The+emergence+of+understanding+in+a+computer+model+of+concepts+and+analogy%2Dmaking%2E+Physica+D%3A+Nonlinear+Phenomena%2C+42%281%2D3%29%3A322%E2%80%93334%2C+1990%2E)
101. Amir Moeini, Jiuqi Wang, Jacob Beck, Ethan Blaser, Shimon Whiteson, Rohan Chandra, and Shangtong Zhang. A survey of in-context reinforcement learning. arXiv preprint arXiv:2502.07978, 2025. — [arXiv:2502.07978](https://arxiv.org/abs/2502.07978) [find](https://scholar.google.com/scholar?q=Amir+Moeini%2C+Jiuqi+Wang%2C+Jacob+Beck%2C+Ethan+Blaser%2C+Shimon+Whiteson%2C+Rohan+Chandra%2C+and+Shangtong+Zhang%2E+A+survey+of+in%2Dcontext+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2502%2E07978%2C+2025%2E+%E2%80%94+%5BarXiv%3A2)
102. Philipp Mondorf and Barbara Plank. Beyond accuracy: Evaluating the reasoning behavior of large language models–a survey. In Conference on Language Modeling, Philadelphia, 2024. — [Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)
103. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. [s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/). arXiv preprint arXiv:2501.19393, 2025.
104. Shashi Narayan, Shay B Cohen, and Mirella Lapata. Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In Association for Computational Linguistics, 2018. — [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/)
105. Allen Newell and Herbert A Simon. Computer simulation of human thinking: A theory of problem solving expressed as a computer program permits simulation of thinking processes. Science, 134(3495):2011–2017, 1961. [find](https://scholar.google.com/scholar?q=Allen+Newell+and+Herbert+A+Simon%2E+Computer+simulation+of+human+thinking%3A+A+theory+of+problem+solving+expressed+as+a+computer+program+permits+simulation+of+thinking+processes%2E+Science%2C+134%283495%29%3A2011%E2%80%932)
106. Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. [Show your work: Scratchpads for intermediate computation with language models](https://md.research.elamur.ai/free/en/papers/al-2021-show-your-work-scratchpads-intermediate/). In ICLR 2022 Workshop DL4C, 2022.
107. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/). Advances in neural information processing systems, 35: 27730–27744, 2022.
108. Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, et al. Balrog: Benchmarking agentic llm and vlm reasoning on games. arXiv preprint arXiv:2411.13543, 2024. — [arXiv:2411.13543](https://arxiv.org/abs/2411.13543) [find](https://scholar.google.com/scholar?q=Davide+Paglieri%2C+Bart%C5%82omiej+Cupia%C5%82%2C+Samuel+Coward%2C+Ulyana+Piterbarg%2C+Maciej+Wolczyk%2C+Akbir+Khan%2C+Eduardo+Pignatelli%2C+%C5%81ukasz+Kuci%C5%84ski%2C+Lerrel+Pinto%2C+Rob+Fergus%2C+et+al%2E+Balrog%3A+Benchmarking+agentic+llm+)
109. Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang. [Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-logic-lm-empowering-large-language/). arXiv preprint arXiv:2305.12295, 2023.
110. Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. [The lambada dataset: Word prediction requiring a broad discourse context](https://md.research.elamur.ai/free/en/papers/paperno-2016-lambada-dataset-word-prediction-requiring/). In Association for Computational Linguistics, 2016.
111. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318, 2002. Manuscript submitted to ACM
112. Arkil Patel, S Bhattamishra, and N Goyal. [Are nlp models really able to solve simple math word problems?](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/) In Association for Computational Linguistics, 2021.
113. Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings. [Refiner: Reasoning feedback on intermediate representations](https://md.research.elamur.ai/free/en/papers/paul-2023-refiner-reasoning-feedback-intermediate-representations/). In Association for Computational Linguistics, 2024.
114. Debjit Paul, Robert West, Antoine Bosselut, and Boi Faltings. [Making reasoning matter: Measuring and improving faithfulness of chain-of-thought reasoning](https://md.research.elamur.ai/free/en/papers/paul-2024-making-reasoning-matter-measuring-improving/). In Association for Computational Linguistics, 2024.
115. Aske Plaat. Research, Re: search & Re-search. PhD thesis, Erasmus University Rotterdam, 1996. [find](https://scholar.google.com/scholar?q=Aske+Plaat%2E+Research%2C+Re%3A+search+%26+Re%2Dsearch%2E+PhD+thesis%2C+Erasmus+University+Rotterdam%2C+1996%2E)
116. Aske Plaat. Learning to play: reinforcement learning and games. Springer Nature, 2020. [find](https://scholar.google.com/scholar?q=Aske+Plaat%2E+Learning+to+play%3A+reinforcement+learning+and+games%2E+Springer+Nature%2C+2020%2E)
117. Aske Plaat. Deep reinforcement learning. Springer, Singapore, 2022. [find](https://scholar.google.com/scholar?q=Aske+Plaat%2E+Deep+reinforcement+learning%2E+Springer%2C+Singapore%2C+2022%2E)
118. Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss, Peter van der Putten, and Kees Joost Batenburg. Agentic large language models, a survey. Journal of Artificial Intelligence Research, arXiv preprint arXiv:2503.[23037, 2025](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). — [arXiv:2503.23037](https://arxiv.org/abs/2503.23037)
119. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. Measuring and narrowing the compositionality gap in language models. In Association for Computational Linguistics, 2023. [find](https://scholar.google.com/scholar?q=Ofir+Press%2C+Muru+Zhang%2C+Sewon+Min%2C+Ludwig+Schmidt%2C+Noah+A+Smith%2C+and+Mike+Lewis%2E+Measuring+and+narrowing+the+compositionality+gap+in+language+models%2E+In+Association+for+Computational+Linguistics%2C+2023)
120. Moschoula Pternea, Prerna Singh, Abir Chakraborty, Yagna Oruganti, Mirco Milletari, Sayli Bapat, and Kebei Jiang. The rl/llm taxonomy tree: Reviewing synergies between reinforcement learning and large language models. Journal of Artificial Intelligence Research, Volume 80; arXiv preprint arXiv:2402.… — [arXiv:2402.01874](https://arxiv.org/abs/2402.01874) [find](https://scholar.google.com/scholar?q=Moschoula+Pternea%2C+Prerna+Singh%2C+Abir+Chakraborty%2C+Yagna+Oruganti%2C+Mirco+Milletari%2C+Sayli+Bapat%2C+and+Kebei+Jiang%2E+The+rl%2Fllm+taxonomy+tree%3A+Reviewing+synergies+between+reinforcement+learning+and+large)
121. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeffrey+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+Ilya+Sutskever%2C+et+al%2E+Language+models+are+unsupervised+multitask+learners%2E+OpenAI+blog%2C+1%288%29%3A9%2C+2019%2E)
122. Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. Scaling language models: Methods, analysis & insights from training gopher. arXiv preprint arXiv:2112.11446, 2021. [find](https://scholar.google.com/scholar?q=Jack+W+Rae%2C+Sebastian+Borgeaud%2C+Trevor+Cai%2C+Katie+Millican%2C+Jordan+Hoffmann%2C+Francis+Song%2C+John+Aslanides%2C+Sarah+Henderson%2C+Roman+Ring%2C+Susannah+Young%2C+et+al%2E+Scaling+language+models%3A+Methods%2C+analysi)
123. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. [Direct preference optimization: Your language model is secretly a reward model](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/). Advances in Neural Information Processing Systems, 36, 2024.
124. Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, and Ziyu Yao. A practical review of mechanistic interpretability for transformer-based language models. arXiv preprint arXiv:2407.02646, 2024. [find](https://scholar.google.com/scholar?q=Daking+Rai%2C+Yilun+Zhou%2C+Shi+Feng%2C+Abulhair+Saparov%2C+and+Ziyu+Yao%2E+A+practical+review+of+mechanistic+interpretability+for+transformer%2Dbased+language+models%2E+arXiv+preprint+arXiv%3A2407%2E02646%2C+2024%2E)
125. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. [Squad: 100,000+ questions for machine comprehension of text](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/). In Association for Computational Linguistics, 2016.
126. Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, et al. [Mathematical discoveries from program search with large language models](https://md.research.elamur.ai/free/en/papers/paredes-2023-mathematical-discoveries-program-search-large/). Nature, 625(7995):468–475, 2024.
127. Subhro Roy and Dan Roth. [Solving general arithmetic word problems](https://md.research.elamur.ai/free/en/papers/roy-2015-solving-general-arithmetic-word-problems/). In Association for Computational Linguistics, 2015.
128. Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. [Unsupervised translation of programming languages](https://md.research.elamur.ai/free/en/papers/lachaux-2020-unsupervised-translation-programming-languages/). Advances in neural information processing systems, 33:20601–20611, 2020.
129. Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, and Tim Genewein. Lmact: A benchmark for in-context imitation learning with long multimodal demonstrations. arXiv preprint arXiv:2412.01441, 2024. — [arXiv:2412.01441](https://arxiv.org/abs/2412.01441) [find](https://scholar.google.com/scholar?q=Anian+Ruoss%2C+Fabio+Pardo%2C+Harris+Chan%2C+Bonnie+Li%2C+Volodymyr+Mnih%2C+and+Tim+Genewein%2E+Lmact%3A+A+benchmark+for+in%2Dcontext+imitation+learning+with+long+multimodal+demonstrations%2E+arXiv+preprint+arXiv%3A2412%2E)
130. Omer Sagi and Lior Rokach. Ensemble learning: A survey. Wiley interdisciplinary reviews: data mining and knowledge discovery, 8(4):e1249, 2018. [find](https://scholar.google.com/scholar?q=Omer+Sagi+and+Lior+Rokach%2E+Ensemble+learning%3A+A+survey%2E+Wiley+interdisciplinary+reviews%3A+data+mining+and+knowledge+discovery%2C+8%284%29%3Ae1249%2C+2018%2E)
131. Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. [A systematic survey of prompt engineering in large language models: Techniques and applications](https://md.research.elamur.ai/free/en/papers/sahoo-2024-systematic-survey-prompt-engineering-large/). arXiv preprint arXiv:2402.07927, 2024.
132. Abulhair Saparov and He He. [Language models are greedy reasoners: A systematic formal analysis of chain-of-thought](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/). In International Conference on Learning Representations, 2023.
133. John Schultz, Jakub Adamek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada Lewis, Anian Ruoss, et al. Mastering board games by external and internal planning with language models. arXiv preprint arXiv:2412.12119, 2024. [find](https://scholar.google.com/scholar?q=John+Schultz%2C+Jakub+Adamek%2C+Matej+Jusup%2C+Marc+Lanctot%2C+Michael+Kaisers%2C+Sarah+Perrin%2C+Daniel+Hennes%2C+Jeremy+Shar%2C+Cannada+Lewis%2C+Anian+Ruoss%2C+et+al%2E+Mastering+board+games+by+external+and+internal+plan)
134. Marwin Segler, M Preuss, and M Waller. Planning chemical syntheses with deep neural networks and symbolic ai. Nature, 555(7698):604–610, 2018. [find](https://scholar.google.com/scholar?q=Marwin+Segler%2C+M+Preuss%2C+and+M+Waller%2E+Planning+chemical+syntheses+with+deep+neural+networks+and+symbolic+ai%2E+Nature%2C+555%287698%29%3A604%E2%80%93610%2C+2018%2E)
135. Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, and Ming Jin. Algorithm of thoughts: Enhancing exploration of ideas in large language models. arXiv preprint arXiv:2308.10379, 2023. [find](https://scholar.google.com/scholar?q=Bilgehan+Sel%2C+Ahmad+Al%2DTawaha%2C+Vanshaj+Khattar%2C+Ruoxi+Jia%2C+and+Ming+Jin%2E+Algorithm+of+thoughts%3A+Enhancing+exploration+of+ideas+in+large+language+models%2E+arXiv+preprint+arXiv%3A2308%2E10379%2C+2023%2E)
136. Rico Sennrich, Barry Haddow, and Alexandra Birch. Improving neural machine translation models with monolingual data. In Association for Computational Linguistics, 2016. [find](https://scholar.google.com/scholar?q=Rico+Sennrich%2C+Barry+Haddow%2C+and+Alexandra+Birch%2E+Improving+neural+machine+translation+models+with+monolingual+data%2E+In+Association+for+Computational+Linguistics%2C+2016%2E)
137. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. [Deepseekmath: Pushing the limits of mathematical reasoning in open language models](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/). arXiv preprint arXiv:2402.03300, 2024.
138. Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, and Qun Liu. Generate & rank: A multi-task framework for math word problems. In Association for Computational Linguistics, 2021. [find](https://scholar.google.com/scholar?q=Jianhao+Shen%2C+Yichun+Yin%2C+Lin+Li%2C+Lifeng+Shang%2C+Xin+Jiang%2C+Ming+Zhang%2C+and+Qun+Liu%2E+Generate+%26+rank%3A+A+multi%2Dtask+framework+for+math+word+problems%2E+In+Association+for+Computational+Linguistics%2C+2021%2E)
139. Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Suzan Verberne, and Zhaochun Ren. Chain of tools: Large language model is an automatic multi-tool learner. arXiv preprint arXiv:2405.16533, 2024. — [arXiv:2405.16533](https://arxiv.org/abs/2405.16533) [find](https://scholar.google.com/scholar?q=Zhengliang+Shi%2C+Shen+Gao%2C+Xiuyi+Chen%2C+Yue+Feng%2C+Lingyong+Yan%2C+Haibo+Shi%2C+Dawei+Yin%2C+Zhumin+Chen%2C+Suzan+Verberne%2C+and+Zhaochun+Ren%2E+Chain+of+tools%3A+Large+language+model+is+an+automatic+multi%2Dtool+learn)
140. Zhengliang Shi, Shen Gao, Xiuyi Chen, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Pengjie Ren, Suzan Verberne, and Zhaochun Ren. Learning to use tools via cooperative and interactive agents. In Association for Computational Linguistics, 2024. [find](https://scholar.google.com/scholar?q=Zhengliang+Shi%2C+Shen+Gao%2C+Xiuyi+Chen%2C+Lingyong+Yan%2C+Haibo+Shi%2C+Dawei+Yin%2C+Zhumin+Chen%2C+Pengjie+Ren%2C+Suzan+Verberne%2C+and+Zhaochun+Ren%2E+Learning+to+use+tools+via+cooperative+and+interactive+agents%2E+In+A)
141. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. [Reflexion: Language agents with verbal reinforcement learning](https://md.research.elamur.ai/free/en/papers/shinn-2023-reflexion-language-agents-verbal-reinforcement/). Advances in Neural Information Processing Systems, 36, 2024. Manuscript submitted to ACM
142. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. [Alfworld: Aligning text and embodied environments for interactive learning](https://md.research.elamur.ai/free/en/papers/shridhar-2020-alfworld-aligning-text-embodied-environments/). In International Conference on Learning Representations, 2021.
143. Mohit Shridhar, Lucas Manuelli, and Dieter Fox. [Cliport: What and where pathways for robotic manipulation](https://md.research.elamur.ai/free/en/papers/shridhar-2021-cliport-where-pathways-robotic-manipulation/). In Conference on robot learning, pages 894–906. PMLR, 2022.
144. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354–359, 2017. [find](https://scholar.google.com/scholar?q=David+Silver%2C+Julian+Schrittwieser%2C+Karen+Simonyan%2C+Ioannis+Antonoglou%2C+Aja+Huang%2C+Arthur+Guez%2C+Thomas+Hubert%2C+Lucas+Baker%2C+Matthew+Lai%2C+Adrian+Bolton%2C+et+al%2E+Mastering+the+game+of+go+without+human+kn)
145. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. [Beyond the imitation game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/). arXiv preprint arXiv:2206.04615, 2022.
146. Richard S Sutton. Learning to predict by the methods of temporal differences. Machine learning, 3:9–44, 1988. [find](https://scholar.google.com/scholar?q=Richard+S+Sutton%2E+Learning+to+predict+by+the+methods+of+temporal+differences%2E+Machine+learning%2C+3%3A9%E2%80%9344%2C+1988%2E)
147. Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT Press, 2018. [find](https://scholar.google.com/scholar?q=Richard+S+Sutton+and+Andrew+G+Barto%2E+Reinforcement+learning%3A+An+introduction%2E+MIT+Press%2C+2018%2E)
148. Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark. [Proofwriter: Generating implications, proofs, and abductive statements over natural language](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/). arXiv preprint arXiv:2012.13048, 2020.
149. Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. [Commonsenseqa: A question answering challenge targeting commonsense knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/). In Association for Computational Linguistics, 2019.
150. Yiming Tan, Dehai Min, Yu Li, Wenbo Li, Nan Hu, Yongrui Chen, and Guilin Qi. Can ChatGPT replace traditional KBQA models? An in-depth analysis of the question answering performance of the GPT LLM family. In International Semantic Web Conference, pages 348–367. Springer, 2023. [find](https://scholar.google.com/scholar?q=Yiming+Tan%2C+Dehai+Min%2C+Yu+Li%2C+Wenbo+Li%2C+Nan+Hu%2C+Yongrui+Chen%2C+and+Guilin+Qi%2E+Can+ChatGPT+replace+traditional+KBQA+models%3F+An+in%2Ddepth+analysis+of+the+question+answering+performance+of+the+GPT+LLM+fami)
151. Yi Tay, Mostafa Dehghani, Vinh Q Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Siamak Shakeri, Dara Bahri, Tal Schuster, et al. [Ul2: Unifying language learning paradigms](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/). In International Conference on Learning Representations, 2023.
152. Gerald Tesauro et al. Temporal difference learning and td-gammon. Communications of the ACM, 38(3):58–68, 1995. [find](https://scholar.google.com/scholar?q=Gerald+Tesauro+et+al%2E+Temporal+difference+learning+and+td%2Dgammon%2E+Communications+of+the+ACM%2C+38%283%29%3A58%E2%80%9368%2C+1995%2E)
153. Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. [Lamda: Language models for dialog applications](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/). arXiv preprint arXiv:2201.08239, 2022.
154. Jason Toy, Josh MacAdam, and Phil Tabor. Metacognition is all you need? using introspection in generative agents to improve goal-directed behavior. arXiv preprint arXiv:2401.10910, 2024. — [arXiv:2401.10910](https://arxiv.org/abs/2401.10910) [find](https://scholar.google.com/scholar?q=Jason+Toy%2C+Josh+MacAdam%2C+and+Phil+Tabor%2E+Metacognition+is+all+you+need%3F+using+introspection+in+generative+agents+to+improve+goal%2Ddirected+behavior%2E+arXiv+preprint+arXiv%3A2401%2E10910%2C+2024%2E+%E2%80%94+%5BarXiv%3A2401)
155. Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems, 36, 2024. — [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/)
156. Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati. On the planning abilities of large language models-a critical investigation. Advances in Neural Information Processing Systems, 36:75993–76005, 2023. — [On the Planning Abilities of Large Language Models : A Critical Investigation](https://md.research.elamur.ai/free/en/papers/valmeekam-2023-planning-abilities-large-language-models/)
157. Frank Van Harmelen, Vladimir Lifschitz, and Bruce Porter. Handbook of knowledge representation. Elsevier, 2008. [find](https://scholar.google.com/scholar?q=Frank+Van+Harmelen%2C+Vladimir+Lifschitz%2C+and+Bruce+Porter%2E+Handbook+of+knowledge+representation%2E+Elsevier%2C+2008%2E)
158. Niki van Stein and Thomas Bäck. Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics. IEEE Transactions on Evolutionary Computation; arXiv preprint arXiv:2405.20132, 2024. — [arXiv:2405.20132](https://arxiv.org/abs/2405.20132) [find](https://scholar.google.com/scholar?q=Niki+van+Stein+and+Thomas+B%C3%A4ck%2E+Llamea%3A+A+large+language+model+evolutionary+algorithm+for+automatically+generating+metaheuristics%2E+IEEE+Transactions+on+Evolutionary+Computation%3B+arXiv+preprint+arXiv%3A2)
159. Fien van Wetten, Aske Plaat, and Max van Duijn. Baba is llm: Reasoning in a game with dynamic rules. arXiv preprint arXiv:2506.19095, 2025. [find](https://scholar.google.com/scholar?q=Fien+van+Wetten%2C+Aske+Plaat%2C+and+Max+van+Duijn%2E+Baba+is+llm%3A+Reasoning+in+a+game+with+dynamic+rules%2E+arXiv+preprint+arXiv%3A2506%2E19095%2C+2025%2E)
160. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). Advances in neural information processing systems, 30, 2017.
161. Marcel VJ Veenman, Bernadette HAM Van Hout-Wolters, and Peter Afflerbach. Metacognition and learning: Conceptual and methodological considerations. Metacognition and learning, 1:3–14, 2006. [find](https://scholar.google.com/scholar?q=Marcel+VJ+Veenman%2C+Bernadette+HAM+Van+Hout%2DWolters%2C+and+Peter+Afflerbach%2E+Metacognition+and+learning%3A+Conceptual+and+methodological+considerations%2E+Metacognition+and+learning%2C+1%3A3%E2%80%9314%2C+2006%2E)
162. Suzan Verberne. Is the search engine of the future a chatbot? Inaugural lecture, Leiden University, 2024. [find](https://scholar.google.com/scholar?q=Suzan+Verberne%2E+Is+the+search+engine+of+the+future+a+chatbot%3F+Inaugural+lecture%2C+Leiden+University%2C+2024%2E)
163. Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. [Glue: A multi-task benchmark and analysis platform for natural language understanding](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/). In Association for Computational Linguistics, 2018.
164. Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. [Superglue: A stickier benchmark for general-purpose language understanding systems](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/). Advances in neural information processing systems, 32, 2019.
165. Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun. [Towards understanding chain-of-thought prompting: An empirical study of what matters](https://md.research.elamur.ai/free/en/papers/wang-2023-understanding-chain-thought-prompting-empirical/). In Association for Computational Linguistics, 2023.
166. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. Transactions of Machine Learning Research; arXiv preprint arXiv:2305.16291, 2024. [find](https://scholar.google.com/scholar?q=Guanzhi+Wang%2C+Yuqi+Xie%2C+Yunfan+Jiang%2C+Ajay+Mandlekar%2C+Chaowei+Xiao%2C+Yuke+Zhu%2C+Linxi+Fan%2C+and+Anima+Anandkumar%2E+Voyager%3A+An+open%2Dended+embodied+agent+with+large+language+models%2E+Transactions+of+Machine)
167. Huaijie Wang, Shibo Hao, Hanze Dong, Shenao Zhang, Yilin Bao, Ziran Yang, and Yi Wu. Offline reinforcement learning for llm multi-step reasoning. arXiv preprint arXiv:2412.16145, 2024. [find](https://scholar.google.com/scholar?q=Huaijie+Wang%2C+Shibo+Hao%2C+Hanze+Dong%2C+Shenao+Zhang%2C+Yilin+Bao%2C+Ziran+Yang%2C+and+Yi+Wu%2E+Offline+reinforcement+learning+for+llm+multi%2Dstep+reasoning%2E+arXiv+preprint+arXiv%3A2412%2E16145%2C+2024%2E)
168. Hui Wang, Michael Emmerich, Mike Preuss, and Aske Plaat. Analysis of hyper-parameters for alphazero-like deep reinforcement learning. International Journal of Information Technology & Decision Making, 22(02):829–853, 2023. [find](https://scholar.google.com/scholar?q=Hui+Wang%2C+Michael+Emmerich%2C+Mike+Preuss%2C+and+Aske+Plaat%2E+Analysis+of+hyper%2Dparameters+for+alphazero%2Dlike+deep+reinforcement+learning%2E+International+Journal+of+Information+Technology+%26+Decision+Making%2C)
169. Jiuqi Wang, Ethan Blaser, Hadi Daneshmand, and Shangtong Zhang. Transformers learn temporal difference methods for in-context reinforcement learning. arXiv preprint arXiv:2405.13861, 2024. — [arXiv:2405.13861](https://arxiv.org/abs/2405.13861) [find](https://scholar.google.com/scholar?q=Jiuqi+Wang%2C+Ethan+Blaser%2C+Hadi+Daneshmand%2C+and+Shangtong+Zhang%2E+Transformers+learn+temporal+difference+methods+for+in%2Dcontext+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2405%2E13861%2C+2024%2E+%E2%80%94+%5BarXiv%3A24)
170. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. [Self-consistency improves chain of thought reasoning in language models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/). In International Conference on Learning Representations, 2023.
171. Yuqing Wang and Yun Zhao. Metacognitive prompting improves understanding in large language models. In Association for Computational Linguistics, 2024. Manuscript submitted to ACM [find](https://scholar.google.com/scholar?q=Yuqing+Wang+and+Yun+Zhao%2E+Metacognitive+prompting+improves+understanding+in+large+language+models%2E+In+Association+for+Computational+Linguistics%2C+2024%2E+Manuscript+submitted+to+ACM)
172. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. [Emergent abilities of large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/). Transactions of Machine Learning Research; arXiv preprint arXiv:2206.07682, 2022.
173. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824–24837, 2022.
174. Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi. [Generating sequences by learning to self-correct](https://md.research.elamur.ai/free/en/papers/welleck-2022-generating-sequences-learning-self-correct/). In International Conference on Learning Representations, 2023.
175. Pengcheng Wen, Jiaming Ji, Chi-Min Chan, Juntao Dai, Donghai Hong, Yaodong Yang, Sirui Han, and Yike Guo. Thinkpatterns-21k: A systematic study on the impact of thinking patterns in llms. arXiv preprint arXiv:2503.12918, 2025. — [arXiv:2503.12918](https://arxiv.org/abs/2503.12918) [find](https://scholar.google.com/scholar?q=Pengcheng+Wen%2C+Jiaming+Ji%2C+Chi%2DMin+Chan%2C+Juntao+Dai%2C+Donghai+Hong%2C+Yaodong+Yang%2C+Sirui+Han%2C+and+Yike+Guo%2E+Thinkpatterns%2D21k%3A+A+systematic+study+on+the+impact+of+thinking+patterns+in+llms%2E+arXiv+prepri)
176. Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao. [Large language models are better reasoners with self-verification](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/). In Association for Computational Linguistics, 2023.
177. Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, et al. A comparative study on reasoning patterns of openai’s o1 model. arXiv preprint arXiv:2410.13639, 2024. — [arXiv:2410.13639](https://arxiv.org/abs/2410.13639) [find](https://scholar.google.com/scholar?q=Siwei+Wu%2C+Zhongyuan+Peng%2C+Xinrun+Du%2C+Tuney+Zheng%2C+Minghao+Liu%2C+Jialong+Wu%2C+Jiachen+Ma%2C+Yizhi+Li%2C+Jian+Yang%2C+Wangchunshu+Zhou%2C+et+al%2E+A+comparative+study+on+reasoning+patterns+of+openai%E2%80%99s+o1+model%2E+arX)
178. Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, and Yoon Kim. Reasoning or reciting? exploring the capabilities and limitations of language models through counterfactual tasks. In 2024 Conference of the North American Chapter of the Associatio… [find](https://scholar.google.com/scholar?q=Zhaofeng+Wu%2C+Linlu+Qiu%2C+Alexis+Ross%2C+Ekin+Aky%C3%BCrek%2C+Boyuan+Chen%2C+Bailin+Wang%2C+Najoung+Kim%2C+Jacob+Andreas%2C+and+Yoon+Kim%2E+Reasoning+or+reciting%3F+exploring+the+capabilities+and+limitations+of+language+mod)
179. Yisheng Xiao, Lijun Wu, Junliang Guo, Juntao Li, Min Zhang, Tao Qin, and Tie-yan Liu. A survey on non-autoregressive generation for neural machine translation and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [find](https://scholar.google.com/scholar?q=Yisheng+Xiao%2C+Lijun+Wu%2C+Junliang+Guo%2C+Juntao+Li%2C+Min+Zhang%2C+Tao+Qin%2C+and+Tie%2Dyan+Liu%2E+A+survey+on+non%2Dautoregressive+generation+for+neural+machine+translation+and+beyond%2E+IEEE+Transactions+on+Pattern+)
180. Ziyang Xiao, Dongxiang Zhang, Yangjun Wu, Lilin Xu, Yuan Jessica Wang, Xiongwei Han, Xiaojin Fu, Tao Zhong, Jia Zeng, Mingli Song, et al. Chain-of-experts: When llms meet complex operations research problems. In 12th International Conference on Learning Representations, 2023. [find](https://scholar.google.com/scholar?q=Ziyang+Xiao%2C+Dongxiang+Zhang%2C+Yangjun+Wu%2C+Lilin+Xu%2C+Yuan+Jessica+Wang%2C+Xiongwei+Han%2C+Xiaojin+Fu%2C+Tao+Zhong%2C+Jia+Zeng%2C+Mingli+Song%2C+et+al%2E+Chain%2Dof%2Dexperts%3A+When+llms+meet+complex+operations+research+p)
181. Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, James Xu Zhao, Min-Yen Kan, Junxian He, and Michael Xie. [Self-evaluation guided beam search for reasoning](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/). Advances in Neural Information Processing Systems, 36, 2024.
182. Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, et al. Towards large reasoning models: A survey on scaling llm reasoning capabilities. arXiv preprint arXiv:2501.[09686, 2025](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/).
183. Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, and Wynne Hsu. [Faithful logical reasoning via symbolic chain-of-thought](https://md.research.elamur.ai/free/en/papers/xu-2024-faithful-logical-reasoning-symbolic-chain/). arXiv preprint arXiv:2405.18357, 2024.
184. Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen, Reynold Cheng, Jinyang Li, Can Xu, Dacheng Tao, and Tianyi Zhou. A survey on knowledge distillation of large language models. arXiv preprint arXiv:2402.13116, 2024. — [arXiv:2402.13116](https://arxiv.org/abs/2402.13116) [find](https://scholar.google.com/scholar?q=Xiaohan+Xu%2C+Ming+Li%2C+Chongyang+Tao%2C+Tao+Shen%2C+Reynold+Cheng%2C+Jinyang+Li%2C+Can+Xu%2C+Dacheng+Tao%2C+and+Tianyi+Zhou%2E+A+survey+on+knowledge+distillation+of+large+language+models%2E+arXiv+preprint+arXiv%3A2402%2E13)
185. Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E Gonzalez, and Bin Cui. [Buffer of thoughts: Thought-augmented reasoning with large language models](https://md.research.elamur.ai/free/en/papers/yang-2024-buffer-thoughts-thought-augmented-reasoning/). In Advances in Neural Information Processing Systems, 2024.
186. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. [Webshop: Towards scalable real-world web interaction with grounded language agents](https://md.research.elamur.ai/free/en/papers/yao-2022-webshop-towards-scalable-real-world/). Advances in Neural Information Processing Systems, 35:20744–20757, 2022.
187. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. [React: Synergizing reasoning and acting in language models](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/). In International Conference on Learning Representations, 2023.
188. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36, 2024. [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Dian+Yu%2C+Jeffrey+Zhao%2C+Izhak+Shafran%2C+Tom+Griffiths%2C+Yuan+Cao%2C+and+Karthik+Narasimhan%2E+Tree+of+thoughts%3A+Deliberate+problem+solving+with+large+language+models%2E+Advances+in+Neural+Informati)
189. Fei Yu, Hongbo Zhang, Prayag Tiwari, and Benyou Wang. [Natural language reasoning, a survey](https://md.research.elamur.ai/free/en/papers/yu-2023-natural-language-reasoning-survey/). ACM Computing Surveys, 2023.
190. Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, et al. [Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/). In Association for Computational Linguistics, 2018.
191. Eric Zelikman, Jesse Mu, Noah D Goodman, and Yuhuai Tony Wu. Star: Self-taught reasoner bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems (NeurIPS), 2022. [find](https://scholar.google.com/scholar?q=Eric+Zelikman%2C+Jesse+Mu%2C+Noah+D+Goodman%2C+and+Yuhuai+Tony+Wu%2E+Star%3A+Self%2Dtaught+reasoner+bootstrapping+reasoning+with+reasoning%2E+Advances+in+Neural+Information+Processing+Systems+%28NeurIPS%29%2C+2022%2E)
192. Yizhuo Zhang, Heng Wang, Shangbin Feng, Zhaoxuan Tan, Xiaochuang Han, Tianxing He, and Yulia Tsvetkov. Can llm graph reasoning generalize beyond pattern memorization? In Association for Computational Linguistics, 2024. [find](https://scholar.google.com/scholar?q=Yizhuo+Zhang%2C+Heng+Wang%2C+Shangbin+Feng%2C+Zhaoxuan+Tan%2C+Xiaochuang+Han%2C+Tianxing+He%2C+and+Yulia+Tsvetkov%2E+Can+llm+graph+reasoning+generalize+beyond+pattern+memorization%3F+In+Association+for+Computational+)
193. Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. [Automatic chain of thought prompting in large language models](https://md.research.elamur.ai/free/en/papers/zhang-2022-automatic-chain-thought-prompting-large/). In International Conference on Learning Representations, 2023.
194. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. [A survey of large language models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/). arXiv preprint arXiv:2303.18223, 2023.
195. Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. [Progressive-hint prompting improves reasoning in large language models](https://md.research.elamur.ai/free/en/papers/zheng-2023-progressive-hint-prompting-improves-reasoning/). arXiv preprint arXiv:2304.09797, 2023.
196. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. [Judging llm-as-a-judge with mt-bench and chatbot arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/). Advances in Neural Information Processing Systems, 36, 2024.
197. Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, and Nan Duan. Analytical reasoning of text. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2306–2319, 2022. [find](https://scholar.google.com/scholar?q=Wanjun+Zhong%2C+Siyuan+Wang%2C+Duyu+Tang%2C+Zenan+Xu%2C+Daya+Guo%2C+Yining+Chen%2C+Jiahai+Wang%2C+Jian+Yin%2C+Ming+Zhou%2C+and+Nan+Duan%2E+Analytical+reasoning+of+text%2E+In+Findings+of+the+Association+for+Computational+Li)
198. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al. [Least-to-most prompting enables complex reasoning in large language models](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/). In International Conference on Learning Representations, 2023. Manuscript submitted…

*Updated 2 November 2026*