Author

Yongjian Guo

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#artificial intelligence May 2026

Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction

This work proposes three exact old-logit acquisition strategies: snapshot-based version tracking, a dedicated old-logit model, and synchronization via partial rollout interruption, and compares their system trade-offs to adopt a revised PPO-EWMA method.

Zhong Guan, Yong-Jian Guo, Haoran Sun et al. · arXiv.org · 4 citations · ⚡1

#artificial intelligence Preprint Feb 2026

RL-VLA$^3$: A Flexible and Asynchronous Reinforcement Learning Framework for VLA Training

RL-VLA is the first fully asynchronous RL training framework tailored specifically for the system-level challenges of VLA training, and achieves throughput improvements of up to 85.2\% over synchronous baselines while maintaining identical sample efficiency, with scalability validated from 8 to 256 GPUs.

Haoran Sun, Yong-Jian Guo, Zhong Guan et al. · 7 citations

Jul 2026

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment

This work proposes Routing-based On-Policy Distillation (ROPD), a novel realignment framework that models the divergence between aligned and compromised output probability distributions rather than fitting specific prompt templates, establishing a new standard for robust LLM realignment.

Yong-Jian Guo, Wanlun Ma, Lingyu Shen et al. · arXiv.org · 0 citations