Wenhao Wu

We propose a token- and task-wise MoE structure to in-context RL models, harnessing architectural advances of MoE to unleash RL's in-context learning capacity.

We learn offline meta-policies from natural language supervision with contrastive language-decision pre-training, aligning text embeddings to comprehend environment dynamics.