RL + Transformer = A General-Purpose Problem Solver
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Show Notes
🤗 Upvotes: 7 | cs.LG, cs.AI
Authors:
Micah Rentschler, Jesse Roberts
Title:
RL + Transformer = A General-Purpose Problem Solver
Arxiv:
http://arxiv.org/abs/2501.14176v1
Abstract:
What if artificial intelligence could not only solve problems for which it was trained but also learn to teach itself to solve new problems (i.e., meta-learn)? In this study, we demonstrate that a pre-trained transformer fine-tuned with reinforcement learning over multiple episodes develops the ability to solve problems that it has never encountered before - an emergent ability called In-Context Reinforcement Learning (ICRL). This powerful meta-learner not only excels in solving unseen in-distribution environments with remarkable sample efficiency, but also shows strong performance in out-of-distribution environments. In addition, we show that it exhibits robustness to the quality of its training data, seamlessly stitches together behaviors from its context, and adapts to non-stationary environments. These behaviors demonstrate that an RL-trained transformer can iteratively improve upon its own solutions, making it an excellent general-purpose problem solver.