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Fuhong Liu

2 accepted papers

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…

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