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Xunliang Cai

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Preprint Sep 2026

Learning What to Practice: Diagnosis-Guided Self-Evolution for Language Models

Self-play supports the self-evolution of language models, but solver performance can plateau or decline across rounds without guidance. Existing unguided methods typically use difficulty, learnability, or diversity signals to keep questions challenging and varied, without identifying which unresolved reasoning weakness...

Xin Wei, Yinze Ding, Fu-Cheng Xiong et al. · 1 citation
#machine learning Preprint Sep 2026

Better Supervision Is Nearby: Neighborhood On-Policy Self-Distillation

On-policy self-distillation (OPSD) trains mathematical reasoning models using a privileged teacher that sees a reference solution and supervises student-sampled prefixes. Standard OPSD uses one fixed parameter setting at every state, but nearby settings may offer additional supervision. We find that local parameter per...

Xin Wei, Yi-Fan Ding, Yoshua Y. Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

DiagEvo: Diagnosis-Guided Self-Evolution via Hierarchical Error Memory

DagEvo is introduced, whose diagnostician extracts recurring error causes from this history and stores them in a hierarchical error-cause memory, whose diagnostician extracts recurring error causes from this history and stores them in a hierarchical error-cause memory.

Xin Wei, Yinze Ding, Yoshua Y. Li et al. · 0 citations
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

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