Shao Zhang

Towards Monotonic Improvement in In-Context Reinforcement Learning

2025 · arXiv (Cornell University)

In-Context Reinforcement Learning (ICRL) has emerged as a promising paradigm for developing agents that can rapidly adapt to new tasks by leveraging past experiences as context, without updating their parameters. Recent approaches train large sequence …