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]
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]
[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]
[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]
[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]
[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]
[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]
[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]
[8] Laskin M, Wang L, Oh J, et al. In-context reinforcement learning with algorithm distillation[J]. arXiv preprint arXiv:2210.14215, 2022.[link]
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]
[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])
[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]
[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]
[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]
[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]
[7] Bai Y, Kadavath S, Kundu S, et al. Constitutional AI: Harmlessness from AI Feedback[J]. arXiv preprint arXiv:2212.08073, 2022.[link]
[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]
[9] Zhu B, Jiao J, Jordan M I. Principled Reinforcement Learning with Human Feedback from Pairwise or $ K $-wise Comparisons[J]. arXiv preprint arXiv:2301.11270, 2023.[link]
Prompt-based but RL related
[1] Madaan A, Tandon N, Gupta P, et al. Self-refine: Iterative refinement with self-feedback[J]. arXiv preprint arXiv:2303.17651, 2023.[link]
[2] Yao S, Zhao J, Yu D, et al. React: Synergizing reasoning and acting in language models[J]. arXiv preprint arXiv:2210.03629, 2022.[link]
[3] Liu H, Sferrazza C, Abbeel P. Languages are rewards: Hindsight finetuning using human feedback[J]. arXiv preprint arXiv:2302.02676, 2023.[link]
[4] Zhang T, Liu F, Wong J, et al. The Wisdom of Hindsight Makes Language Models Better Instruction Followers[J]. arXiv preprint arXiv:2302.05206, 2023.[link]
[5] Chen X, Lin M, Schärli N, et al. Teaching Large Language Models to Self-Debug[J]. arXiv preprint arXiv:2304.05128, 2023.[link]
[6] Liu R, Yang R, Jia C, et al. Training Socially Aligned Language Models in Simulated Human Society[J]. arXiv preprint arXiv:2305.16960, 2023.
[link]
[7] Chen L, Wang L, Dong H, et al. Introspective Tips: Large Language Model for In-Context Decision Making[J]. arXiv preprint arXiv:2305.11598, 2023.[link]
Code
[1] DeepSpeed Chat RLHF [link]
[2] TRLX [link]
[3] PKU-Beaver [link]
[4] ColossalAI [link]
/LLM-with-RL-papers