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Colton Bishop

2 accepted papers

2025

Training Language Models to Self-Correct via Reinforcement Learning

ICLR 2025oral

Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correction typically depend on either multiple models, a more advanced model, or additional forms of super…

Cited by 113SourcePDFScholar
2024

RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

ICML 2024poster

Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but gathering high-quality preference labels is expensive. RL from AI Feedback (RLAIF), introduced in Bai et al. (2022b), offers a promising alternative that trains…

Cited by 99SourcePDFScholar