40 citations · 63 across the 6 of their papers we have counts for
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cs.LG2024★ 3 cited
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)…
cs.LG2019★ 15 cited
HIGhER : Improving instruction following with Hindsight Generation for Experience Replay
Geoffrey Cideron, Mathieu Seurin, Florian Strub +1
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may…