RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards

arXiv — cs.LG•Friday, October 31, 2025 at 4:00:00 AM

A new approach called Binary Flexible Feedback (RLBFF) aims to enhance Reinforcement Learning by bridging the gap between Human Feedback and Verifiable Rewards. This is significant because it addresses the limitations of existing methods, such as the interpretability issues of Human Feedback and the narrow focus of Verifiable Rewards. By integrating these paradigms, RLBFF could lead to more effective and reliable training of large language models, ultimately improving their performance and usability.

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