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Life sciences · Preprint
arXiv · August 17, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes Task Specialization Fine-Tuning (TSFT), a novel online framework that allocates a constrained fine-tuning budget across multiple task regions in contextual reinforcement learning by predicting performance and solving a discrete optimization problem. Experiments across combinatorial optimization, continuous control, and LLM fine-tuning report that TSFT outperforms baselines in task coverage and approaches oracle performance, but the work is unrefereed and lacks formal statistical validation, comparator specifications, or peer-reviewed confirmation.
Preprint. Contextual reinforcement learning agents tested on combinatorial optimization, continuous control, and LLM fine-tuning tasks. Intervention: Task Specialization Fine-Tuning (TSFT) framework: pretraining a single policy followed by fine-tuning multiple policies with budget allocation via parametric performance prediction and integer linear programming.. Compared with: Baselines (named in full paper, not specified in abstract); oracle performance.
TSFT significantly outperforms baselines in task coverage across diverse decision domains TSFT approaches oracle performance in the tested domains
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This is an unrefereed arXiv preprint presenting a novel algorithmic framework for contextual reinforcement learning; it has not undergone peer review.
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Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While prior works often train from scratch and rely on either multi-task learning for a single policy or strategically training multiple policies, we advocate for a unified alternative: pretraining a single policy with good initial performance, followed by fine-tuning multiple policies for task specialization. This new paradigm, however, introduces unique challenges, such as heterogeneous marginal returns and sample inefficiency. This raises a critical research question: given a pretrained policy and a constrained budget, how much fine-tuning should each task region receive to enable sample-efficient CRL? To this end, we propose 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. Extensive experiments across diverse decision domains, including combinatorial optimization, continuous control, and LLM fine-tuning, demonstrate that TSFT significantly outperforms baselines in task coverage and approaches oracle performance. Our work charts a new direction for model-based CRL, aligning with the modern pretrain-finetune era.
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