LLMs Are In-Context Reinforcement Learners

• URL: http://arxiv.org/abs/2410.05362v1
• Date: Mon, 7 Oct 2024 17:45:00 GMT
• Title: LLMs Are In-Context Reinforcement Learners
• Authors: Giovanni Monea, Antoine Bosselut, Kianté Brantley, Yoav Artzi,
• Abstract summary: Large Language Models (LLMs) can learn new tasks through in-context supervised learning (i.e., ICL) This work studies if this ability extends to in-context reinforcement learning (ICRL), where models are not given gold labels in context, but only their past predictions and rewards. We propose an algorithm to address this deficiency by increasing test-time compute, as well as a compute-bound approximation.
• Score: 30.192422586838997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) can learn new tasks through in-context supervised learning (i.e., ICL). This work studies if this ability extends to in-context reinforcement learning (ICRL), where models are not given gold labels in context, but only their past predictions and rewards. We show that a naive application of ICRL fails miserably, and identify the root cause as a fundamental deficiency at exploration, which leads to quick model degeneration. We propose an algorithm to address this deficiency by increasing test-time compute, as well as a compute-bound approximation. We use several challenging classification tasks to empirically show that our ICRL algorithms lead to effective learning from rewards alone, and analyze the characteristics of this ability and our methods. Overall, our results reveal remarkable ICRL abilities in LLMs.

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