We aim to evaluate Large Language Models (LLMs) for embodied decision making.

In this work, we investigate how an adversary can exploit instruction tuning by injecting specific instruction-following examples into the training data that intentionally changes the model's behavior.

These skills are refined and updated through an iterative comparison strategy, enabling efficient adaptation to unseen environments.

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