Author

Kai-Xiang Zhao

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Preprint Sep 2026

Do Defenses Against LLM Extraction Work Across Attacks? A Lifecycle Benchmark of Black-Box Model Extraction

Large language models (LLMs) deployed through text-only APIs face model extraction risks, as adversaries can collect their responses to train surrogates that reproduce their capabilities. While prior work has developed diverse attacks and defenses, evaluations remain fragmented across access assumptions, model configur...

Shu-Ze Liu, Kai-Xiang Zhao, Run-Yang Xu et al. · 0 citations

#machine learning Preprint Sep 2026

REVO: Rollout-Efficient Off-Policy Distillation via Variance-Guided Reuse

On-policy distillation (OPD) trains language models using dense token-level teacher supervision on student-generated trajectories. However, its reliance on frequently refreshed student rollouts often incurs substantial generation cost. We introduce REVO, an off-policy distillation framework that improves rollout effici...

Yu-Xiao Yang, Shang-Zhe Li, Tian-Run Yu et al. · 0 citations

#machine learning Preprint Sep 2026

An RL View of OPD: Least Square Policy Distillation for Sample-Efficient LLM Reasoning

An RL perspective on OPD is provided that offers both a principled interpretation and a practical route toward more effective and rollout-efficient language model distillation.

Shang-Zhe Li, Yu-Xiao Yang, Tian-Run Yu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Rethinking Training-Inference Mismatch in LLM Reinforcement Learning: Where It Arises and How to Correct It

CIS replaces the unbounded second moment that governs the error of exact importance sampling with a term bounded by a constant, at the cost of a bias controlled by the truncated excess, in evaluation across three mixture-of-experts models and five mathematical reasoning benchmarks.

Tian-Run Yu, Kai-Xiang Zhao, Shang-Zhe Li et al. · 0 citations

#machine learning Preprint Sep 2026

When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation

We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify \emph{termination-token mismatch} between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama, and Gemma, th...

Yu-Xiao Yang, Tian-Run Yu, Shang-Zhe Li et al. · 4 citations

#artificial intelligence Preprint May 2026

Self-Correction Can Amplify Hallucinations: Fact-Level Repair with Graph-Based Evidence Routing in Multimodal Generation

TIGER is presented, an inference-time framework that redesigns feedback for localized repair that reduces unsupported content while preserving task quality and a CrisisFACTS case study suggests that the same repair mechanism can improve grounding in multi-source settings.

Kaixiang Zhao, Tianrun Yu, Shawn Huang et al. · 0 citations