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Dual-Entropy Enhanced Policy Optimization (DEEPO), a dual-stage enhancement combining signal variance regularization with gradient preconditioning with semantic-entropy-triggered expert prefixes inject grounded continuations on high-uncertainty queries, providing direct supervision and restoring advantage variance.
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.
Experiments with GRPO and GiGPO on ALFWorld, WebShop, and SearchQA show that both axes provide independent gains and that their combi- nation consistently achieves the strongest overall performance across model scales.
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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