---
title: "Who cites Samy Jelassi — 143 papers of our library"
description: "143 papers of the Elamur Research library cite the works of Samy Jelassi, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/samy-jelassi/cited-by/"
---
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# Who cites Samy Jelassi

148 citations of their works from 143 papers of our library (of the 149,439 whose bibliographies we read) · 47 with a page here, the most cited first · [← Samy Jelassi](https://md.research.elamur.ai/free/en/authors/samy-jelassi/)

1. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv:2303.12712**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,439 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
2. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/)
3. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paper**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 216 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
4. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 213 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
5. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Alexander Wettig, Howard Yen, et al.*2025 · 89 citations*
6. **[Learning to Reason under Off-Policy Guidance](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jianhao Yan, Yafu Li, Zican Hu, et al.*2025 · 68 citations*
7. [**An Empirical Study of Mamba-based Language Models arXiv:2406.07887**Roger Waleffe, Wonmin Byeon, Duncan Riach, et al.*2024 · 67 citations*](https://md.research.elamur.ai/free/en/papers/waleffe-2024-empirical-study-mamba-based-language/)
8. [**What Algorithms can Transformers Learn? A Study in Length Generalization arXiv:2310.16028**Hattie Zhou, Arwen Bradley, Etai Littwin, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/)
9. [**LAB-Bench: Measuring Capabilities of Language Models for Biology Research arXiv:2407.10362**Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/laurent-2024-lab-bench-measuring-capabilities/)
10. [**Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling arXiv:2406.07522**Liliang Ren, Yang Liu, Yadong Lu, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/ren-2024-samba-simple-hybrid-state-space/)
11. **[ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Mingjie Liu, Shizhe Diao, Ximing Lu, et al.*2025 · 58 citations*
12. [**Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process arXiv:2407.20311**Tian Ye, Zicheng Xu, Yuanzhi Li, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-physics-language-models-part-2/)
13. [**Repeat After Me: Transformers are Better than State Space Models at Copying arXiv:2402.01032**Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/)
14. [**Spurious Rewards: Rethinking Training Signals in RLVR arXiv:2506.10947**Rulin Shao, Shuyue Stella Li, Rui Xin, et al.*2025 · 53 citations*](https://md.research.elamur.ai/free/en/papers/shao-2025-spurious-rewards-rethinking-training-signals/)
15. [**Kimi Linear: An Expressive, Efficient Attention Architecture arXiv:2510.26692**Kimi Team, Yu Zhang, Zongyu Lin, et al.*2025 · 51 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-linear-expressive-efficient/)
16. [**Hymba: A Hybrid-head Architecture for Small Language Models arXiv:2411.13676**Xin Dong, Yonggan Fu, Shizhe Diao, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-hymba-hybrid-head-architecture-small/)
17. [**Zamba: A Compact 7B SSM Hybrid Model arXiv:2405.16712**Paolo Glorioso, Quentin Anthony, Yury Tokpanov, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/glorioso-2024-zamba-compact-7b-ssm-hybrid/)
18. **[Long-Context Language Modeling with Parallel Context Encoding](https://md.research.elamur.ai/free/en/papers/yen-2024-long-context-language-modeling-parallel/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Howard Yen, Tianyu Gao, Danqi Chen*2024 · 44 citations*
19. [**Physics of Language Models: Part 1, Learning Hierarchical Language Structures arXiv:2305.13673**Zeyuan Allen-Zhu, Yuanzhi Li*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/allen-zhu-2023-physics-language-models-part-1/)
20. [**GPT Can Solve Mathematical Problems Without a Calculator arXiv:2309.03241**Zhen Yang, Ming Ding, Qingsong Lv, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-gpt-solve-mathematical-problems-calculator/)
21. **[Transformers Can Do Arithmetic with the Right Embeddings](https://md.research.elamur.ai/free/en/papers/mcleish-2024-transformers-arithmetic-right-embeddings/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sean McLeish, Arpit Bansal, Alex Stein, et al.*2024 · 36 citations*
22. [**Positional Description Matters for Transformers Arithmetic arXiv:2311.14737**Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-positional-description-matters-transformers-arithmetic/)
23. [**Transformers Can Achieve Length Generalization But Not Robustly arXiv:2402.09371**Yongchao Zhou, Uri Alon, Xinyun Chen, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-transformers-achieve-length-generalization-but/)
24. [**Distillation Scaling Laws arXiv:2502.08606**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
25. **[Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers](https://md.research.elamur.ai/free/en/papers/alfarano-2024-global-lyapunov-functions-long-standing/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alberto Alfarano, François Charton, Amaury Hayat*2024 · 19 citations*
26. [**RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval arXiv:2402.18510**Kaiyue Wen, Xingyu Dang, Kaifeng Lyu*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/wen-2024-rnns-transformers-yet-key-bottleneck/)
27. [**Outcome-based Exploration for LLM Reasoning arXiv:2509.06941**Yuda Song, Julia Kempe, Remi Munos*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-outcome-based-exploration-llm-reasoning/)
28. [**Chain-of-Thought Reasoning is a Policy Improvement Operator arXiv:2309.08589**Hugh Zhang, David C. Parkes*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-chain-thought-reasoning-policy-improvement/)
29. [**Associative Recurrent Memory Transformer arXiv:2407.04841**Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/rodkin-2024-associative-recurrent-memory-transformer/)
30. [**SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training arXiv:2310.02227**Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, et al.*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/meidani-2023-snip-bridging-mathematical-symbolic-numeric/)
31. [**ERNIE 5.0 Technical Report arXiv:2602.04705**Haifeng Wang, Hua Wu, Tian Wu, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2026-ernie-5-0-technical/)
32. [**Improving Length-Generalization in Transformers via Task Hinting arXiv:2310.00726**Pranjal Awasthi, Anupam Gupta*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/awasthi-2023-improving-length-generalization-transformers-task/)
33. [**Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks arXiv:2406.01895**Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/sabbaghi-2024-explicitly-encoding-structural-symmetry-key/)
34. **[Why Do More Experts Fail? A Theoretical Analysis of Model Merging](https://md.research.elamur.ai/free/en/papers/wang-2026-why-do-more-experts-fail/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zijing Wang, Xingle Xu, YongKang Liu, et al.*2026 · 2 citations*
35. **[Improving Dialect Robustness in Large Language Models via LoRA and Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/maheshwari-2026-improving-dialect-robustness-large-language/) [Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)](https://md.research.elamur.ai/free/en/venues/vardial/)**Sanjh Maheshwari, Aniket Singh Rajpoot, Oana Cocarascu, et al.*2026 · 1 citation*
36. **[Inferring Events from Time Series using Language Models](https://md.research.elamur.ai/free/en/papers/tan-2026-inferring-events-time-series-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingtian Tan, Mike A Merrill, Zachary Gottesman, et al.*2026 · 1 citation*
37. **[StateX: Enhancing RNN Recall via Post-training State Expansion](https://md.research.elamur.ai/free/en/papers/shen-2026-statex-enhancing-rnn-recall-post/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingyu Shen, Yingfa Chen, Zhen Leng Thai, et al.*2026 · 1 citation*
38. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
39. [**AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing**Sarubi Thillainathan, Ji-Ung Lee, Michael Sullivan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/thillainathan-2026-authormix-modular-authorship-style-transfer/)
40. **[Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/shen-2026-does-rlvr-extend-reasoning-boundaries/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minghe Shen, Zhuo Zhi, Chonghan Liu, et al.*2026*
41. **[Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts](https://md.research.elamur.ai/free/en/papers/wang-2026-don-t-tell-answer-truly/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyi Wang, Jinyi Han, Zishang Jiang, et al.*2026*
42. **[FeedTrans: Enhance Machine Translation via Feedback](https://md.research.elamur.ai/free/en/papers/sun-2026-feedtrans-enhance-machine-translation-feedback/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zengkui Sun, Jiali Zeng, Jiaan Wang, et al.*2026*
43. **[GlossaGen: Making Academic Translation Smarter with Glossing](https://md.research.elamur.ai/free/en/papers/wang-2026-glossagen-making-academic-translation-smarter/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zixiao Wang, Duzhen Zhang, Juntian Zhang, et al.*2026*
44. **[Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement](https://md.research.elamur.ai/free/en/papers/sun-2026-learning-cognition-enhancing-rl-efficiency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zexu Sun, Yongcheng Zeng, Erxue Min, et al.*2026*
45. **[Mixed-Policy GRPO for Text-to-SQL with Off-Policy Data Generation](https://md.research.elamur.ai/free/en/papers/sterbentz-2026-mixed-policy-grpo-text-sql/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Marko Sterbentz, Michael Glass, Nhan H Pham, et al.*2026*
46. **[On the Representation Geometry of LoRA Model Merging](https://md.research.elamur.ai/free/en/papers/lu-2026-representation-geometry-lora-model-merging/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenyang Lu, Jiaru Li, Jinman Zhao, et al.*2026*
47. **[Tailored Primitive Initialization is the Secret Key to Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yao-2026-tailored-primitive-initialization-is-secret/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihang Yao, Guangtao Zeng, Raina Wu, et al.*2026*

96 more citing papers of our library have no page here yet.

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