EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
Summary
EvoPolicyGym is a benchmark for evaluating how autonomous agents iteratively improve policies through feedback in interactive environments, with GPT-5.5 achieving top performance.
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Paper page - EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
Source: https://huggingface.co/papers/2607.02440 Published on Jul 2
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Abstract
Autonomous agents evaluate policy improvement through iterative editing within fixed budgets, revealing that successful policy evolution requires both task-specific mechanisms and feedback-constrained refinement.
Autonomous agentsare increasingly expected to improveexecutable policiesthrough feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We introduce AutonomousPolicy 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 providestrajectory-level diagnosticsthat distinguish how agents allocate budget, convert feedback intoparametric tuning. These analyses show that strong autonomouspolicy evolutiondepends not only on isolated task wins, but on discoveringtask-appropriate mechanismsand refining policies under bounded feedback.
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