contextual-learning

The paper introduces Task Specialization Fine-Tuning (TSFT) for contextual reinforcement learning, a framework that uses a simple parametric model and integer linear programming to allocate fine-tuning budgets efficiently, significantly outperforming baselines in task coverage.

This paper proposes a reinforcement learning approach to enable large language models to translate unseen languages by leveraging in-context linguistic knowledge, outperforming in-context learning and supervised fine-tuning.