Rui-Han A. Li

Downstream-Aware Context Selection for Online In-Context Reinforcement Learning

In-context reinforcement learning (ICRL) enables large language model agents to adapt to new environments using their interaction history without updating model parameters. However, repeatedly conditioning on growing histories can lead to substantial token cost. We propose a bounded-history context-management framework...