Papers by Mohammad Azar
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion (2024.emnlp-main)
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Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Bill Wu, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Azar, Olivier Pietquin, Matthieu Geist
| Challenge: | Reinforcement Learning (RL) is a method used to fine tune Large Language Models (LLMs) using a reward model trained from preference data to better align with human judgment. |
| Approach: | They propose a Reinforcement Learning (RL) algorithm that can estimate the optimal policy even from off-policy data. |
| Outcome: | The proposed algorithm can estimate the optimal policy even from off-policy data. |