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Xin Guo

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Preprint Aug 2026

A Token-Level Analysis of Sampled-Token Reverse-KL On-Policy Distillation

On-policy distillation (OPD) supervises a student on its own trajectories with token-level signals from a frozen teacher, yet how a sampled loss allocates updates across tokens remains poorly understood. We analyze the gradient of the per-token K2 estimator of reverse KL with respect to the student logits. The $\ell_1$...

Bing Shao, Jia-Zheng Zhang, Long Ma et al. · 5 citations
Conference Open access Sep 2026

MathCritique: Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision

A critique-in-the-loop self-improvement method that incorporates critique-based supervision into the actor’s self-training process and improves the actor’s exploration efficiency and solution diversity, especially on challenging queries, leading to a stronger actor model.

Zhi-Heng Xi, Dingwen Yang, Jixuan Huang et al. · 0 citations
#natural language process... Preprint Sep 2026

CORAL: An LLM-Native Harness for Production Recommender Systems

Production recommender systems shape what billions of people see, and sustaining their performance requires continual optimization: as content, user behavior, and upstream models shift, the choices governing retrieval, ranking, and serving must be revisited. Traditionally, human engineers test such changes through onli...

Muhammad Azhar, Yu-Hang Zhou, Gilbert Jiang et al. · 1 citation
Conference Open access 2026

Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing

To mitigate a critical imbalance during the exploration-and-learning process, this work approaches head-tail re-balance during the exploration-and-learning process from two perspectives: distribution-reshaping and trajectory-resampling.

Xin Guo, Zhiheng Xi, Yiwen Ding et al. · 1 citation
Conference Open access Jul 2026

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

AgentGym2 is presented, a new evaluation framework with task instances grounded in real-world end-to-end working demands that measures agents'ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information.

Zhiheng Xi, Dingwen Yang, Jiaqi Liu et al. · 1 citation

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