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Google DeepMind's SCoRe Achieves 15.6% Gain in Self-Correction for Language Models

Sep 20, 2024, 01:26 PM

Google DeepMind has developed a new multi-turn chain of thought online reinforcement learning (RL) approach called SCoRe to improve the self-correction capabilities of large language models (LLMs). This method uses entirely self-generated data and has achieved state-of-the-art performance in self-correction. The approach has shown a 15.6% gain in self-correction for reasoning problems from the MATH dataset and a 9.1% improvement in other areas. The research, authored by A Kumar, V Zhuang, R Agarwal, and Y Su, suggests that training with off-the-shelf datasets for RL is less effective compared to using on-policy data from the model being fine-tuned.