3 citations · 8 across the 4 of their papers we have counts for
3 papers · 1 filter
BOND: Aligning LLMs with Best-of-N Distillation
Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot +17
Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-t…
WARP: On the Benefits of Weight Averaged Rewarded Policies
Alexandre Ramé, Johan Ferret, Nino Vieillard +7
Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs) by encouraging their generations to have high rewards, using a reward model trained on human p…
WARM: On the Benefits of Weight Averaged Reward Models
Alexandre Ramé, Nino Vieillard, Léonard Hussenot +4
Aligning large language models (LLMs) with human preferences through reinforcement learning (RLHF) can lead to reward hacking, where LLMs exploit failures in the reward model (RM)…