most citedBack to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

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cs.LG2024★ 1 cited

Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Arash Ahmadian, Chris Cremer, Matthias Gallé +5

AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models. Proximal…