不会做题的AI，却能当老师
一个自己动手做任务时频频犯错的 AI，反而能当老师，把另一个小模型教得更好。研究者让 6 个模型在 ALFWorld 这类家务任务环境里当「执行者」:它们都会被更容易的岔路勾走，漏掉必须先做的关键一步。可换成「事后裁判」——等整段过程跑完再回头看每一步有没有用——同一个模型就能认出哪些动作真正推进了目标；而且问「这一步有没有帮助」比问「任务会不会成功」更能分出好坏动作。拿这个裁判当老师去教一个 1.5B 的小模型，成功率从 72.8% 提到 82.9%，网购环境 WebShop 上从 56.8% 提到 71.4%。道理是：动手时必须提前猜后果，事后评判却能看见后果本身。它不是你明天能用上的东西。
📄 原文摘要(英文)
Decision models answer a question with a typed output, such as a choice among options with a probability for each, so one model can choose an agent's actions, evaluate actions already taken, or score every action to teach a policy with reinforcement learning. Prior work has studied them mainly as actors, judging their usefulness by how well they act. We ask whether a decision model that acts poorly can still teach well. We compare three decision models and three general LLMs as actors and judges on ALFWorld, and use JEV as a teacher on ALFWorld and WebShop. As actors, all six models are pulled away from a subgoal that must come first by an easier one. As judges that see the whole trajectory, the same models credit the steps toward that subgoal, and asking whether each action helped, rather than whether the task will succeed, separates useful from useless actions. As a teacher, JEV raises a Qwen2.5-1.5B policy over GRPO from 72.8% to 82.9% success on ALFWorld and from 56.8% to 71.4% on WebShop. These results suggest that a poor actor can still teach well, because judging an action after the episode ends reveals the consequences that acting must anticipate. Code is available at https://github.com/ZJU-REAL/decision-agents.