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Self-improvement through post-training methods such as iterative preference learning has been acclaimed for enhancing the problem-solving capabilities (e.g., mathematical reasoning) of Large Language Models (LLMs) without human intervention.
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
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Training verifiers to solve math word problems, 2021
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G. Tevet and J. Berant · 2021
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LoRA: Low-rank adaptation of large language models
E. J. Hu, yelong shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2022
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Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
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Training language models to follow instructions with human feedback, 2022
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STar: Bootstrapping reasoning with reasoning
E. Zelikman, Y. Wu, J. Mu, and N. Goodman · 2022
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Large language models can self-improve
J. Huang, S. Gu, L. Hou, Y. Wu, X. Wang, H. Yu, and J. Han · 2023
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A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, et al · 2023
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Efficient memory management for large language model serving with pagedattention
W. Kwon, Z. Li, S. Zhuang, Y. Sheng, L. Zheng, C. H. Yu, J. E. Gonzalez, H. Zhang, and I. Stoica · 2023
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Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, S. Gupta, B. P. Majumder, K. Hermann, S. Welleck, A. Yazdanbakhsh, and P. Clark · 2023
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Self-play fine-tuning converts weak language models to strong language models, 2024
Z. Chen, Y. Deng, H. Yuan, K. Ji, and Q. Gu · 2024
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CRITIC: Large language models can self-correct with tool-interactive critiquing
Z. Gou, Z. Shao, Y. Gong, yelong shen, Y. Yang, N. Duan, and W. Chen · 2024
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Large language models cannot self-correct reasoning yet
J. Huang, X. Chen, S. Mishra, H. S. Zheng, A. W. Yu, X. Song, and D. Zhou · 2024
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Understanding the effects of RLHF on LLM generalisation and diversity
R. Kirk, I. Mediratta, C. Nalmpantis, J. Luketina, E. Hambro, E. Grefenstette, and R. Raileanu · 2024
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RLAIF: Scaling reinforcement learning from human feedback with AI feedback, 2024
H. Lee, S. Phatale, H. Mansoor, K. R. Lu, T. Mesnard, J. Ferret, C. Bishop, E. Hall, V. Carbune, and A. Rastogi · 2024
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Self-alignment with instruction backtranslation
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Direct preference optimization: Your language model is secretly a reward model
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Autogpt: Build & use ai agents
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Beyond human data: Scaling self-training for problem-solving with language models, 2023
A. Singh, J. D. Co-Reyes, R. Agarwal, A. Anand, P. Patil, X. Garcia, P. J. Liu, J. Harrison, J. Lee, K. Xu, A. Parisi, A. Kumar, A. Alemi, A. Rizkowsky, A. Nova, B. Adlam, B. Bohnet, G. Elsayed, H. Sedghi, I. Mordatch, I. Simpson, I. Gur, J. Snoek, J. Pennington, J. Hron, K. Kenealy, K. Swersky, K. Mahajan, L. Culp, L. Xiao, M. L. Bileschi, N. Constant, R. Novak, R. Liu, T. Warkentin, Y. Qian, Y. Bansal, E. Dyer, B. Neyshabur, J. Sohl-Dickstein, and N. Fiedel · 2023
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Principle-driven self-alignment of language models from scratch with minimal human supervision
Z. Sun, Y. Shen, Q. Zhou, H. Zhang, Z. Chen, D. Cox, Y. Yang, and C. Gan · 2023
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Stanford alpaca: An instruction-following llama model
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto · 2023
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Llama 2: Open foundation and fine-tuned chat models
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al · 2023
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Self-instruct: Aligning language models with self-generated instructions
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi · 2023
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Iterative reasoning preference optimization, 2024
R. Y. Pang, W. Yuan, K. Cho, H. He, S. Sukhbaatar, and J. Weston · 2024
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SALMON: Self-alignment with principle-following reward models
Z. Sun, Y. Shen, H. Zhang, Q. Zhou, Z. Chen, D. D. Cox, Y. Yang, and C. Gan · 2024
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Preference fine-tuning of llms should leverage suboptimal, on-policy data, 2024
F. Tajwar, A. Singh, A. Sharma, R. Rafailov, J. Schneider, T. Xie, S. Ermon, C. Finn, and A. Kumar · 2024
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Metamath: Bootstrap your own mathematical questions for large language models
L. Yu, W. Jiang, H. Shi, J. YU, Z. Liu, Y. Zhang, J. Kwok, Z. Li, A. Weller, and W. Liu · 2024
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Self-rewarding language models, 2024
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Llamafactory: Unified efficient fine-tuning of 100+ language models
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