Credit Where It's Due: How CAPO Brings Verifiable Precision to LLM Reasoning
When training Large Language Models (LLMs) to reason, reinforcement learning has proven to be a powerful yet blunt instrument. Most methods reduce the entire model output to a single pass/fail reward, applying that verdict to every token—regardless of whether it contributed to success or failure. This makes credit assignment vague, verifiability weak, and learning inefficient. Enter CAPO (Credit Assignment Policy Optimization), a method that shifts the paradigm: it brings verifiable, fine-grained credit assignment to the token level, using LLMs themselves as judgment agents. ...