Author

YeHan Yang

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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

Preprint Aug 2026

Autonomy-of-Heads: Data-Free Sparse Attention from Frozen Query-Key Geometry

This work proposes Autonomy-of-Heads (AoH), a data-free method that identifies retrieval and streaming heads from the spectral geometry of query-key projections that retains 96.5% of Full Attention performance on average while reducing prefill and decode latency by up to 41.4 and 66.0%, respectively, and KV-cache memor...

Ye-Han Yang, Jun-Yuan Shang, Yang Li et al. · 0 citations