The podcast discusses a recent paper on supervised pretraining for in-context reinforcement learning using transformers. The paper explores how transformers can efficiently implement various reinforcement learning algorithms and the implications for decision-making in AI systems. The key takeaways for engineers/specialists from the paper are: Supervised pretraining with transformers can efficiently approximate prevalent RL algorithms, transformers demonstrate the potential for near-optimal regret bounds, and the research highlights the importance of model capacity and distribution divergence in in-context reinforcement learning. Read full paper: https://arxiv.org/abs/2310.08566 Tags: Reinforcement Learning, Transformers, Meta-Learning, Deep Neural Networks