Fetching the paper…
Reading the bibliography…
Distribution matching methods for language model alignment such as Generation with Distributional Control (GDC) and Distributional Policy Gradient (DPG) have not received the same level of attention in reinforcement learning from human feedback (RLHF) as contrastive methods such as Sequence Likelihood Calibration (SLiC), Direct Preference Optimization (DPO) and its variants.
Distributional reinforcement learning for energy-based sequential models
Parshakova, T., Andreoli, J., and Dymetman, M · 1912
Earlier work this paper cites.
Learning to summarize from human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D. M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2009
Earlier work this paper cites.
Importance sampling: a review
Tokdar, S. T. and Kass, R. E · 2010
Earlier work this paper cites.
A distributional approach to controlled text generation
Khalifa, M., Elsahar, H., and Dymetman, M · 2012
Earlier work this paper cites.
Monte Carlo theory, methods and examples
Owen, A. B · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Controlling linguistic style aspects in neural language generation
Ficler, J. and Goldberg, Y · 2017
Earlier work this paper cites.
Tuning recurrent neural networks with reinforcement learning
Jaques, N., Gu, S., Turner, R. E., and Eck, D · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Tl;dr: Mining reddit to learn automatic summarization
Völske, M., Potthast, M., Syed, S., and Stein, B · 2017
Earlier work this paper cites.
Reinforcement learning and control as probabilistic inference: Tutorial and review
Levine, S · 2018
Earlier work this paper cites.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G · 2019
Earlier work this paper cites.
Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B. and Komatsuzaki, A · 2021
Cited alongside, same era.
Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Bai, Y., Jones, A., and et al., K. N · 2022
Cited alongside, same era.
On reinforcement learning and distribution matching for fine-tuning language models with no catastrophic forgetting
Korbak, T., Elsahar, H., Kruszewski, G., and Dymetman, M · 2022
Cited alongside, same era.
GPT-4 technical report, 2023
OpenAI, :, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al · 2023
Later among the works it cites.
Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
Later among the works it cites.
Zephyr: Direct distillation of lm alignment
Tunstall, L., Beeching, E., Lambert, N., Rajani, N., Rasul, K., Belkada, Y., Huang, S., von Werra, L., Fourrier, C., Habib, N., et al · 2023
Later among the works it cites.
A comprehensive capability analysis of gpt-3 and gpt-3.5 series models, 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Quark: Controllable text generation with reinforced unlearning
Lu, X., Welleck, S., Hessel, J., Jiang, L., Qin, L., West, P., Ammanabrolu, P., and Choi, Y · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
Defining and characterizing reward hacking
Skalse, J., Howe, N. H. R., Krasheninnikov, D., and Krueger, D · 2022
Cited alongside, same era.
Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
Cited alongside, same era.
Camels in a changing climate: Enhancing lm adaptation with tulu 2
Ivison, H., Wang, Y., Pyatkin, V., Lambert, N., Peters, M., Dasigi, P., Jang, J., Wadden, D., Smith, N. A., Beltagy, I., et al · 2023
Cited alongside, same era.
Pretraining language models with human preferences
Korbak, T., Shi, K., Chen, A., Bhalerao, R. V., Buckley, C., Phang, J., Bowman, S. R., and Perez, E · 2023
Cited alongside, same era.
Statistical rejection sampling improves preference optimization
Liu, T., Zhao, Y., Joshi, R., Khalman, M., Saleh, M., Liu, P. J., and Liu, J · 2023
Cited alongside, same era.
Ye, J., Chen, X., Xu, N., Zu, C., Shao, Z., Liu, S., Cui, Y., Zhou, Z., Gong, C., Shen, Y., Zhou, J., Chen, S., Gui, T., Zhang, Q., and Huang, X · 2023
Later among the works it cites.
RRHF: Rank responses to align language models with human feedback without tears
Yuan, Z., Yuan, H., Tan, C., Wang, W., Huang, S., and Huang, F · 2023
Later among the works it cites.
SLiC-HF: Sequence likelihood calibration with human feedback
Zhao, Y., Joshi, R., Liu, T., Khalman, M., Saleh, M., and Liu, P. J · 2023
Later among the works it cites.
Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E., and Stoica, I · 2023
Later among the works it cites.
A general theoretical paradigm to understand learning from human preferences
Azar, M. G., Guo, Z. D., Piot, B., Munos, R., Rowland, M., Valko, M., and Calandriello, D · 2024
Closest in time.
Kto: Model alignment as prospect theoretic optimization
Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., and Kiela, D · 2024
Closest in time.
Reference-free monolithic preference optimization with odds ratio
Hong, J., Lee, N., and Thorne, J · 2024
Closest in time.
Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., de Las Casas, D., Hanna, E. B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L. R., Saulnier, L., Lachaux, M., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T. L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2024
Closest in time.