2025
Mixture-of-Experts Meets In-Context Reinforcement Learning
NeurIPS 2025poster
In-context reinforcement learning (ICRL) has emerged as a promising paradigm for adapting RL agents to downstream tasks through prompt conditioning. However, two notable challenges remain in fully harnessing in-context learning within RL domains: the intrinsic multi-modality of the state-action-rewa…