Author

Han Wu

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#artificial intelligence Preprint Oct 2026

Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence

Reinforcement learning has become the foundational paradigm for enhancing the reasoning capabilities of LLMs. Yet the dominant approach, Proximal Policy Optimization (PPO), relies on a geometrically degenerate clipping proxy that cannot adapt its resistance to policy shifts, leading to premature clipping and degraded e...

Zhi-Cheng Cai, Xin-Yuan Guo, Han Wu et al. · 0 citations

Jul 2026

Weak-to-Strong Generalization via Direct On-Policy Distillation

Direct On-Policy Distillation (Direct-OPD) is proposed, which transfers the teacher's RL-induced policy shift instead of running sparse-reward RL on the target model and consistently leverages weaker teachers to improve stronger target models.

Shiyuan Feng, Huan Gao, Haohan Chi et al. · arXiv.org · 8 citations · ⚡1

Preprint Jul 2026

Spectral Rewiring for Exploration, Purification, and Model Merging

Subspace-Aligned Rewiring (SAR) shows that extracting reasoning-effective updates from parameter geometry can serve as a training-free mechanism to improve reasoning and multi-domain performance.

Zhilong Zhang, Hongli Yu, Huan Gao et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Open-MOPD: Diagnosing and Fixing Capability Imbalance in Multi-Teacher On-Policy Distillation

Open-MOPD, a principled framework incorporating token-share balancing, gap-aware dynamic budget allocation, and student reward refresh, systematically restore cross-domain balance, elevating headroom recovery from 35.6% to 83.4% in a single deployable student.

Huan Gao, Haohan Chi, Yong Yan et al. · 12 citations · ⚡6