Step-level Reward

A new Zhejiang University paper shows that a decision model, JEV, which completes only 42.9% of ALFWorld tasks on its own, can still act as a step-level grader and lift another agent's success rate from 72.8% to 82.9%. The grader reviews a full trajectory after the attempt and its per-action scores are folded into GRPO training, sidestepping the uniform credit assignment of outcome-only rewards and the cost of per-environment labels or per-action LLM judges. The authors argue step graders should be selected by how well they judge recorded runs, not by how well they act.