arXiv AI August 19, 2026 By Jianan Zhou, Jung-Hoon Cho, Tianyue Zhou, Han Zheng, Jie Zhang, Roy Dong, Yining Ma, Cathy Wu

Task Specialization Fine-Tuning for Contextual Reinforcement Learning

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The paper introduces Task Specialization Fine-Tuning (TSFT), an online framework that allocates a limited fine‑tuning budget across multiple task regions in Contextual Reinforcement Learning. TSFT predicts fine‑tuning performance with a simple parametric model and solves the budget allocation problem exactly using integer linear programming. Experiments on combinatorial optimization, continuous control, and LLM fine‑tuning show that TSFT outperforms baselines in task coverage and approaches oracle performance.

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