This is a linkpost for https://arxiv.org/abs/2210.14215
More evidence for the point "generative models can contain agents", or specifically "generative models trained to imitation agents can learn to behave agentically". However, not more evidence for the claim "generative models trained to be generators / generative models trained to be useful tools will suddenly learn an internal agent". Does that seem right?
This post argues there is no inner mesa-optimizer here:
This model is a proof of concept of powerful implicit mesa optimizer, which is evidence towards "current architectures could be easily inner misaligned".
Notably the model was trained across multiple episodes to pick up on RL improvement.
Though the usual inner misalignment means that it’s trying to gain more reward in future episodes by forgoing reward in earlier ones, but I don’t think this is evidence for that.
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.
Abstract:
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.