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Large Language Models as Generalizable Policies for Embodied Tasks
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2026-02-15 18:02:29
raw text
Large Language Models as Generalizable Policies for Embodied Tasks Large Language Models as Generalizable Policies for Embodied Tasks Andrew Szot , Max Schwarzer , Harsh Agrawal , Bogdan Mazoure , Walter Talbott , Katherine Metcalf , Natalie Mackraz , Devon Hjelm , Alexander Toshev arXiv Code Abstract We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text instructions and visual egocentric observations and output actions directly in the environment. Using reinforcement learning, we train LLaRP to see and act solely through environmental interactions. We show that LLaRP is robust to complex paraphrasings of task instructions and can generalize to new tasks that require novel optimal behavior. In particular, on 1,000 unseen tasks it achieves 42% success rate,...
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