ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning

ProAS is a plug-and-play progress-aware advantage shaping framework that uses a dynamically evolving Agentic Reference Graph (ARG) to achieve fine-grained credit assignment, mitigating the problem in critic-free algorithms such as GRPO where trajectory-level advantages are broadcast equally to all actions.