---
title: "Who cites Thinking Fast and Slow with Deep — 95 citing papers"
description: "95 papers of the Elamur Research library cite «Thinking Fast and Slow with Deep Learning and Tree Search», found in their bibliographies…"
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---
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# Who cites «Thinking Fast and Slow with Deep Learning and Tree Search»

95 papers of our library cite it (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · [← back to «Thinking Fast and Slow with Deep Learning and Tree Search»](https://md.research.elamur.ai/free/en/papers/anthony-2017-thinking-fast-slow-deep-learning/)

1. [**Training language models to follow instructions with human feedback arXiv:2203.02155**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 10,224 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
2. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
3. [**Mastering chess and shogi by self-play with a general reinforcement learning algorithm***2017 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/al-2017-mastering-chess-shogi-self-play/)
4. [**STaR: Bootstrapping Reasoning With Reasoning**Eric Zelikman, Yuhuai Wu, Jesse Mu, et al.*2022 · 777 citations*](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)
5. **[AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yann Dubois, Xuechen Li, Rohan Taori, et al.*2023 · 456 citations*
6. [**Solving math word problems with process- and outcome-based feedback arXiv:2211.14275**Jonathan Uesato, Nate Kushman, Ramana Kumar, et al.*2022 · 355 citations*](https://md.research.elamur.ai/free/en/papers/uesato-2022-solving-math-word-problems-process/)
7. [**Generative Language Modeling for Automated Theorem Proving arXiv:2009.03393**Stanislas Polu, Ilya Sutskever*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/polu-2020-generative-language-modeling-automated-theorem/)
8. [**Scalable agent alignment via reward modeling: a research direction arXiv:1811.07871**Jan Leike, David Krueger, Tom Everitt, et al.*2018 · 227 citations*](https://md.research.elamur.ai/free/en/papers/leike-2018-scalable-agent-alignment-reward-modeling/)
9. [**Recursively Summarizing Books with Human Feedback arXiv:2109.10862**Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.*2021 · 208 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-recursively-summarizing-books-human-feedback/)
10. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
11. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
12. [**TALM: Tool Augmented Language Models arXiv:2205.12255**Aaron Parisi, Yao Zhao, Noah Fiedel*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2022-talm-tool-augmented-language-models/)
13. [**Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models arXiv:2312.06585**Avi Singh, John D. Co-Reyes, Rishabh Agarwal, et al.*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/singh-2023-human-data-scaling-self-training/)
14. **[ReFT: Reasoning with Reinforced Fine-Tuning](https://md.research.elamur.ai/free/en/papers/trung-2024-reft-reasoning-reinforced-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luong Trung, Xinbo Zhang, Zhanming Jie, et al.*2024 · 134 citations*
15. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
16. **[SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://md.research.elamur.ai/free/en/papers/lin-2023-swiftsage-generative-agent-fast-slow/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bill Yuchen Lin, Yicheng Fu, Karina Yang, et al.*2023 · 110 citations*
17. [**Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning arXiv:2410.08146**Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.*2024 · 93 citations*](https://md.research.elamur.ai/free/en/papers/setlur-2024-rewarding-progress-scaling-automated-process/)
18. [**Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving arXiv:2502.07640**Yong Lin, Shange Tang, Bohan Lyu, et al.*2025 · 84 citations*](https://md.research.elamur.ai/free/en/papers/lin-2025-goedel-prover-frontier-model-open/)
19. **[Lean Workbook: A large-scale Lean problem set formalized from natural language math problems](https://md.research.elamur.ai/free/en/papers/ying-2024-lean-workbook-large-scale-lean/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Huaiyuan Ying, Zijian Wu, Yihan Geng, et al.*2024 · 80 citations*
20. [**Reinforcement Learning via Self-Distillation arXiv:2601.20802**Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.*2026 · 74 citations*](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-self-distillation/)
21. [**Teaching Large Language Models to Reason with Reinforcement Learning arXiv:2403.04642**Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-teaching-large-language-models-reason/)
22. [**InternLM2.5-StepProver: Advancing Automated Theorem Proving via Critic-Guided Search arXiv:2410.15700**Zijian Wu, Suozhi Huang, Zhejian Zhou, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-internlm2-5-stepprover-advancing-automated/)
23. **[Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://md.research.elamur.ai/free/en/papers/zelikman-2023-parsel-algorithmic-reasoning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Eric Zelikman, Qian Huang, Gabriel Poesia, et al.*2023 · 56 citations*
24. [**Rewarding Chatbots for Real-World Engagement with Millions of Users arXiv:2303.06135**Robert Irvine, Douglas Boubert, Vyas Raina, et al.*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/irvine-2023-rewarding-chatbots-real-world-engagement/)
25. **[Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels](https://md.research.elamur.ai/free/en/papers/franken-2024-self-supervised-alignment-mutual-information/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jan-Philipp Franken, Eric Zelikman, Rafael Rafailov, et al.*2024 · 16 citations*
26. **[Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening](https://md.research.elamur.ai/free/en/papers/he-2025-rewarding-unlikely-lifting-grpo-beyond/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Andre Wang He, Daniel Fried, Sean Welleck*2025 · 15 citations*
27. [**Language Models are Bounded Pragmatic Speakers: Understanding RLHF from a Bayesian Cognitive Modeling Perspective arXiv:2305.17760**Khanh Nguyen*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nguyen-2023-language-models-bounded-pragmatic-speakers/)

68 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/anthony-2017-thinking-fast-slow-deep-learning/) — Thinking Fast and Slow with Deep Learning and Tree Search