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Language Models are Few-Shot Butlers
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Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. Hence, these autoregressive models constitute ideal agents to operate in text-based environments where language understanding and generative capabilities are essential. Nonetheless, collecting expert demonstrations in such environments is a time-consuming endeavour. We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment. We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment.
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Cited by 2 Pith papers
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Embodied CoT Distillation From LLM To Off-the-shelf Agents
DeDer distills LLM chain-of-thought reasoning into a two-tier small-language-model policy (rationale writer plus planner) and reports state-of-the-art ALFRED success rates for small-model embodied agents.
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WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents
WALL-E 2.0 improves LLM agents by encoding learned environment rules as executable code that corrects an LLM world model, lifting ALFWorld success to 98%.
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