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Graph-Conditioned On-Policy Agent Distillation is introduced, which enriches an off-the-shelf teacher's scoring context with execution evidence, and indexes repeated teacher executions by shared states while preserving complete successful and failed histories.
Reflection on Visual Evidence (ReVuE), an on-policy distillation method for visual agents that translates trajectory-level evidence diagnosis into targeted token-level supervision, guiding students to improve their visual evidence acquisition and reasoning.
It is shown that on-policy reinforcement learning with verifiable rewards (RLVR) can improve the current objective while making successful behaviors for later objectives too rare to sample and reinforce, and effective rewardable support is defined as successful trajectories reachable within a fixed rollout budget.
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