Author

Tianjin Huang

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#machine learning Preprint Oct 2026

When Non-Adversarial Noise Hurts Learning: Benchmarking Skill Self-Evolution in Agents

Large language model agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rules and affect later tasks. Existing evaluations largely examine clean learning or perturbations encountered after skills are frozen, leaving robustness during self-evol...

Tao Yu, Zhong-Tian Luo, Yu-Chen Long et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

This work introduces Co-RL, a framework in which multiple decoupled models, sharing no parameters, are simultaneously optimized through RL using rewards derived from their peers, and shows that unsupervised reasoning can emerge through cooperative multi-agent training.

Yunhao Yang, Yuexin Bian, Yun-Jie Tian et al. · 1 citation