想看看AI智能体怎么自己迭代策略吗？EvoPolicyGym这个新基准让GPT-5.5跑了16个强化学习环境，全部拿到前两名。
EvoPolicyGym是一个基于16个紧凑强化学习环境的基准，用于评测智能体在固定交互预算下迭代改进策略的能力。GPT-5.5在全部16个环境中取得最强综合排名，并在每个环境中进入前两名。该基准还提供轨迹级诊断，分析智能体如何分配预算、将反馈转化为参数调优。结果表明，自主策略演化不仅依赖单次任务胜出，更需在有限反馈下发现任务适配机制并细化策略。
EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We introduce Autonomous Policy Evolution, a controlled evaluation setting in which a harness-model agent repeatedly edits an executable policy system under a fixed interaction budget. We instantiate this setting in EvoPolicyGym, a benchmark built from compact interactive RL environments that evaluates how agents iteratively improve explored policies. On the EvoPolicyGym suite, GPT-5.5 achieves the strongest aggregate rank score and top-two performance on all 16 environments. Beyond leaderboard results, EvoPolicyGym also provides trajectory-level diagnostics that distinguish how agents allocate budget, convert feedback into parametric tuning. These analyses show that strong autonomous policy evolution depends not only on isolated task wins, but on discovering task-appropriate mechanisms and refining policies under bounded feedback.