Authors train transformers to imitate the trajectory of reinforcement learning (RL) algorithms. Find that the transformers learn to do in-context RL (that is, the transformers implement an RL algorithm)---the authors check this by having the transformers solve new RL tasks. Indeed, the transformers can sometimes do better than the RL algorithms they're trained to imitate.
Seems like more evidence for the "a generative model contain agents" point.
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model. Algorithm Distillation treats learning to reinforcement learn as an across-episode sequential prediction problem. A dataset of learning histories is generated by a source RL algorithm, and then a causal transformer is trained by autoregressively predicting actions given their preceding learning histories as context. Unlike sequential policy prediction architectures that distill post-learning or expert sequences, AD is able to improve its policy entirely in-context without updating its network parameters. We demonstrate that AD can reinforcement learn in-context in a variety of environments with sparse rewards, combinatorial task structure, and pixel-based observations, and find that AD learns a more data-efficient RL algorithm than the one that generated the source data.