New AI Method Internalizes Outcome Supervision into Process Supervision for Reasoning

A new research paper on arXiv (2605.05226) proposes a method for reinforcement learning in reasoning tasks that internalizes outcome supervision into process supervision. The approach enables models to automatically extract process-level learning signals by identifying, correcting, and refining intermediate reasoning steps, addressing the challenge of sparse outcome-level feedback. This method aims to overcome limitations of existing approaches that rely on costly external process supervision or struggle with precise credit assignment in sequence-level optimization.