Author

Cathy Wu

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#artificial intelligence May 2026

Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning

METIS (METacognitive Internalized Self-judgment), a novel framework that internalizes curriculum judgment as a native capability, establishes a simple, closed-loop, and highly efficient curriculum internalization paradigm for LLM reinforcement fine-tuning.

Hang Zheng, Yi-Ning Ma, K. Gunasekaran et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Aug 2026

Task Specialization Fine-Tuning for Contextual Reinforcement Learning

Task Specialization Fine-Tuning (TSFT), an online framework that predicts fine-tuning performance with a simple parametric model and exactly solves the resulting discrete budget allocation problem via integer linear programming, is proposed.

Jianan Zhou, Jung-Hoon Cho, Tianyue Zhou et al. · 0 citations