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A collection of LLM with RL papers
| Date | Stars |
|---|---|
| 2026-07-31 | 280 |
| 2026-08-18 | 281 |
| 2026-09-21 | 282 |
| 2026-10-21 | 281 |
| 2026-11-04 | 281 |
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# LLM-with-RL-papers A collection of LLM with RL related papers for instruction following, reasoning, decision making, continuous improvement and self improvement etc. ## Review [1] Yang S, Nachum O, Du Y, et al. **Foundation Models for Decision Making: Problems, Methods, and Opportunities**[J]. arXiv preprint arXiv:2303.04129, 2023.[[link]](https://arxiv.org/pdf/2303.04129) ## RL without Human Feedback [1] Le H, Wang Y, Gotmare A D, et al. **Coderl: Mastering code generation through pretrained models and deep reinforcement learning**[J]. Advances in Neural Information Processing Systems, 2022, 35: 21314-21328. [[link]](https://proceedings.neurips.cc/paper_files/paper/2022/file/8636419dea1aa9fbd25fc4248e702da4-Paper-Conference.pdf) [2] Shojaee P, Jain A, Tipirneni S, et al. **Execution-based Code Generation using Deep Reinforcement Learning**[J]. arXiv preprint arXiv:2301.13816, 2023.[[link]](https://arxiv.org/pdf/2301.13816) [3] Uesato J, Kushman N, Kumar R, et al. **Solving math word problems with process-and outcome-based feedback**[J]. arXiv preprint arXiv:2211.14275, 2022.[[link]](https://arxiv.org/pdf/2211.14275) [4] Deng M, Wang J, Hsieh C P, et al. **Rlprompt: Optimizing discrete text prompts with reinforcement learning**[J]. arXiv preprint arXiv:2205.12548, 2022.[[link]](https://arxiv.org/pdf/2205.12548) [5] Carta T, Romac C, Wolf T, et al. **Grounding large language models in interactive environments with online reinforcement learning**[J]. arXiv preprint arXiv:2302.02662, 2023.[[link]](https://arxiv.org/pdf/2302.02662) [6] Mezghani L, Bojanowski P, Alahari K, et al. **Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions**[J]. arXiv preprint arXiv:2304.11063, 2023.[[link]](https://arxiv.org/pdf/2304.11063) [7] Dubois Y, Li X, Taori R, et al. **AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback**[J]. arXiv preprint arXiv:2305.14387, 2023. [[link]](https://arxiv.org/pdf/2305.14387) [8] Laskin M, Wang L, Oh J, et al. **In-context reinforcement learning with algorithm distillation**[J]. arXiv preprint arXiv:2210.14215, 2022.[[link]](https://arxiv.org/pdf/2210.14215) ## RLHF(RL with Human Feedback) [1] Christiano P F, Leike J, Brown T, et al. **Deep reinforcement learning from human preferences**[J]. Advances in neural information processing systems, 2017, 30. [[link]](https://proceedings.neurips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf) [2] Ziegler D M, Stiennon N, Wu J, et al. **Fine-tuning language models from human preferences**[J]. arXiv preprint arXiv:1909.08593, 2019.[[link]](https://arxiv.org/pdf/1909.08593.pdf)) [3] Stiennon N, Ouyang L, Wu J, et al. **Learning to summarize with human feedback**[J]. Advances in Neural Information Processing Systems, 2020, 33: 3008-3021.[[link]](https://arxiv.org/pdf/2009.01325.pdf) [4] Ouyang L, Wu J, Jiang X, et al. **Training language models to follow instructions with human feedback**[J]. Advances in Neural Information Processing Systems, 2022, 35: 27730-27744.[[link]](https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf) [5] Nakano R, Hilton J, Balaji S, et al. **Webgpt: Browser-assisted question-answering with human feedback**[J]. arXiv preprint arXiv:2112.09332, 2021.[[link]](https://arxiv.org/pdf/2112.09332) [6] Bai Y, Jones A, Ndousse K, et al. **Training a helpful and harmless assistant with reinforcement learning from human feedback**[J]. arXiv preprint arXiv:2204.05862, 2022.[[link]](https://arxiv.org/pdf/2204.05862) [7] Bai Y, Kadavath S, Kundu S, et al. **Constitutional AI: Harmlessness from AI Feedback**[J]. arXiv preprint arXiv:2212.08073, 2022.[[link]](https://arxiv.org/pdf/2212.08073) [8] Ganguli D, Askell A, Schiefer N, et al. **The capacity for moral self-correction in large language models**[J]. arXiv preprint arXiv:2302.07459, 2023.[[link]](https://arxiv.org/pdf/2302.07459) [9] Zhu B, Jiao J, Jorda
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:af4db4cf2c827d8b, llm:Repository description: 'A collection of LLM with RL papers' (collection of research papers on combining large language models with reinforcement learning).
matched fp:af4db4cf2c827d8b, llm:Repository description: 'A collection of LLM with RL papers' (collection of research papers on combining large language models with reinforcement learning).
matched fp:af4db4cf2c827d8b, llm:Repository description: 'A collection of LLM with RL papers' (collection of research papers on combining large language models with reinforcement learning).