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UECR-GRPO is introduced, which integrates verifier and teacher signals within a single GRPO-style update at both the response and token levels and uses the signed teacher--old-policy token gap to redistribute the verifier-derived component.
Reinforcement learning for LLM agents involves two distinct optimization di- mensions: how environment feedback is exploited within a trajectory, and how complete trajectories are aggregated across a batch. We formulate these dimen- sions as Intra-Trajectory Feedback Attribution and Inter-Trajectory Objec- tive Aggrega...
TASPO, which converts privileged supervision into outcome-grounded action credit, is introduced and indicates that TASPO reduces supervision mismatch and that action-level assignment stabilizes the policy optimization process.
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