Fixing GRPO's credit assignment problem without evaluating every step (arxiv.org)

Researchers have introduced ProVer, an innovative framework aimed at addressing the limitations of Group Relative Policy Optimization (GRPO) in reinforcement learning, particularly in the context of training large language model agents. GRPO faces a challenge with its uniform credit assignment, which fails to differentiate the impact of pivotal decisions from less significant ones. ProVer enhances this by focusing on significant decision points during the learning process, employing a mechanism that allows an agentic judge to evaluate successful versus unsuccessful trajectories. It then verifies these crucial segments without the need for exhaustive evaluation of every intermediate state, thereby streamlining the training process. The significance of ProVer lies in its ability to improve the accuracy of credit assignment while minimizing computational overhead. By incorporating insights from model assessment only where necessary, the framework demonstrates substantial performance gains over traditional GRPO techniques, achieving improvements of 9.91% and 7.12% in two model sizes (Qwen3.5-2B and Qwen3.5-4B) on benchmarks like ALFWorld, WebShop, and SearchQA. This approach enables more effective policy training, underscoring the potential for enhancing reinforcement learning methodologies by selectively targeting impactful decisions, thus marking a step forward in the optimization of agentic learning processes.