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Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs research paper by Cohere, 2024

Cohere · Feb 22, 2024 · Alignment and safety · 18 upvotes 2 years ago

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What it shows

Reinforcement Learning from Human Feedback (RLHF) benefits from simpler REINFORCE-style optimization methods over computationally expensive PPO or "RL-free" methods, enhancing performance and reducing cost in large language models.

Hugging Face's summary; not yet checked by hand.

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About this paper
Authors
Arash Ahmadian, Chris Cremer, Matthias Gallé and 4 more
arXiv
2402.14740 · PDF
Citations
Not counted yet · Semantic Scholar
Upvotes
18 · Hugging Face
Lab
Cohere · on Companies · on Acquisitions · on Releases

Changes

What changed
New paperAdded to the listSep 26, 2026today

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