570 citations · 2.6k across the 75 of their papers we have counts for
4 papers · 1 filter
Averaging log-likelihoods in direct alignment
Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar +8
To better align Large Language Models (LLMs) with human judgment, Reinforcement Learning from Human Feedback (RLHF) learns a reward model and then optimizes it using regularized RL…
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub +8
Reinforcement Learning (RL) has been used to finetune Large Language Models (LLMs) using a reward model trained from preference data, to better align with human judgment. The recen…
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua +3
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a dee…
MusicRL: Aligning Music Generation to Human Preferences
Geoffrey Cideron, Sertan Girgin, Mauro Verzetti +11
We propose MusicRL, the first music generation system finetuned from human feedback. Appreciation of text-to-music models is particularly subjective since the concept of musicality…