Fixing GRPO's credit assignment problem without evaluating every step
ProVer is a new framework for agentic reinforcement learning that identifies and verifies pivotal decision segments to solve GRPO's credit assignment problem without evaluating every step.
summary is enough The abstract clearly details the ProVer method and benchmarks, making a full reading unnecessary unless you plan to implement it.
summary
Group Relative Policy Optimization struggles with agentic reinforcement learning because it applies trajectory-level advantages uniformly across all tokens. The proposed ProVer framework addresses this by using an agentic judge to spot divergent trajectory segments between successful and failed rollouts. It then verifies these segments by measuring terminal success rate differences rather than exhaustively evaluating every intermediate state. Experiments on benchmarks like ALFWorld and WebShop show consistent performance gains over baseline GRPO across multiple model scales.
key points
- ProVer uses an agentic judge to contrast successful and failed trajectories and propose pivotal decision segments.
- Proposed segments are verified by estimating advantage from terminal success rate differences of policy continuations.
- Outperforms standard GRPO by 9.91 percent on Qwen3.5-2B and 7.12 percent on Qwen3.5-4B across benchmarks like ALFWorld and WebShop.
who should read it
Machine learning researchers and engineers working on reinforcement learning for language model agents.
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