This AI Paper Has Moves: How Language Models Groove into Offline Reinforcement Learning with ‘LaMo’ Dance Steps and Few-Shot Learning

Researchers introduce Language Models for Motion Control (LaMo), a framework using Large Language Models (LLMs) for offline reinforcement learning. It leverages pre-trained LLMs to enhance RL policy learning, employing Decision Transformers (DT) initialized with LLMs and LoRA fine-tuning. LaMo outperforms existing methods in sparse-reward tasks and narrows the gap between value-based offline RL and decision […]

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