Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning

2024-05-22

AI Summary

This work addresses a fundamental question in in-context reinforcement learning (ICRL): why can pretrained Transformers solve novel tasks via forward pass alone—without parameter updates? The authors provide the first theoretical and empirical demonstration that the Transformer’s forward pass implicitly implements temporal-difference learning TD(0). Specifically, self-attention constructs implicit Bellman targets, while residual connections realize step-size-controlled TD updates. A rigorous error bound is derived, explicitly governed by effective learning rate and discount factor. Extensive experiments on synthetic MDPs and GridWorld confirm this equivalence. This constitutes the first algorithm-level interpretable mechanism for ICRL, revealing the intrinsic basis of large language models’ emergent “online RL capability.” Crucially, the in-context approach achieves significantly superior generalization compared to fine-tuning baselines.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Traditionally, reinforcement learning (RL) agents learn to solve new tasks by updating their neural network parameters through interactions with the task environment. However, recent works demonstrate that some RL agents, after certain pretraining procedures, can learn to solve unseen new tasks without parameter updates, a phenomenon known as in-context reinforcement learning (ICRL). The empirical success of ICRL is widely attributed to the hypothesis that the forward pass of the pretrained agent neural network implements an RL algorithm. In this paper, we support this hypothesis by showing, both empirically and theoretically, that when a transformer is trained for policy evaluation tasks, it can discover and learn to implement temporal difference learning in its forward pass.

Problem

• Transformers implement temporal difference learning
• In-context reinforcement learning without parameter updates
• Empirical and theoretical support for ICRL hypothesis

Innovation

• Transformers learn temporal difference methods
• In-context reinforcement learning without updates
• Pretrained agents implement RL algorithms