Weak AI Models Can Grade Steps to Train Stronger Agents, Zhejiang University Finds
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.
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Rohan Paul (@rohanpaul_ai) · 2026-11-14 18
AI model that is itself bad at a task can still train another AI to do it well.
New paper shows that a decision model called JEV, an AI model built to pick between options instead of writing text, completes only 42.9% of simulated household tasks on its own, yet by reviewing another AI’s moves afterward it raised that AI’s success from 72.8% to 82.9%.
New Zhejiang University paper shows that an AI model that is bad at a task can still train another AI to do it well, by reviewing each of its moves after the attempt ends.
Agent RL usually rewards only the final outcome, so every step gets the same credit. Scoring each step usually needs per-environment labels or an LLM judge at every action.
They tested decision models, which return a choice with probabilities instead of text, as actors and as step graders on ALFWorld and WebShop. As a grader, the model sees the whole run and scores whether each action helped, and those scores are added to GRPO training.
The decision model JEV completes only 42.9% of ALFWorld tasks when acting. As a grader, it lifts a Qwen2.5-1.5B policy to 82.9% success, up from 72.8% with GRPO outcome rewards alone.
Choose step graders by how well they judge recorded runs, not how well they act, and ask about each action, not the outcome.
