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Vision-Language Models Provide Promptable Representations for Reinforcement Learning
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Vision-Language Models Provide Promptable Representations for Reinforcement Learning
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2026-02-20 17:30:18
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Vision-Language Models Provide Promptable Representations for Reinforcement Learning Vision-Language Models Provide Promptable Representations for Reinforcement Learning William Chen 1 , Oier Mees 1 , Aviral Kumar 2 , Sergey Levine 1 , 1 UC Berkeley, 2 Google DeepMind Paper Notebook Example PR2L provides a flexible way for shaping representations for reinforcement learning with VLMs. Abstract Humans can quickly learn new behaviors by leveraging background world knowledge. In contrast, agents trained with reinforcement learning (RL) typically learn behaviors from scratch. We thus propose a novel approach that uses the vast amounts of general and indexable world knowledge encoded in vision-language models (VLMs) pre-trained on Internet-scale data for embodied RL. We initialize policies with VLMs by using them as promptable representations: embeddings that encode seman...
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