Author

Binbin Zheng

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Jul 2026

Deconstructing Off-Policy Ratios: Entropy-Scaled Trust Regions for Asynchronous Reinforcement Learning

The Entropy-Scaled Trust Region (ESTR), which scales each token’s off-policy deviation by its local entropy, requiring no auxiliary forward passes or explicit version-switch detection, consistently outperforms existing asynchronous methods and achieves the best train-inference consistency.

Guan-Qun Zhao, Zi-Jun Xie, Bin-Bin Zheng et al. · arXiv.org · 3 citations

#artificial intelligence Preprint Sep 2026

BRACE: Anchored Bellman-Residual Correction for Stale Critics in Asynchronous RL

BRACE is proposed, an anchored Bellman-residual correction for stale value models that bounds the correction horizon to a prefix of policy tokens and anchors a constant-weight Monte-Carlo tail beyond it, which separates policy correction from reward propagation.

Guan-Qun Zhao, Zi-Jun Xie, Bin-Bin Zheng et al. · 0 citations

Jul 2026

Group-Reflective Self-Distillation for Agentic Reinforcement Learning

Group-Reflective Self-Distillation (GRSD), which derives capability-aligned and outcome-discriminative guidance from the policy's own verified rollouts, and refines turn-level credit assignment by modulating outcome-based advantages while preserving the verifier-determined learning direction.

Binbin Zheng, Zi-Jun Xie, Guan-Qun Zhao et al. · arXiv.org · 1 citation