Explore an in-depth interview with authors Machel Reid and Yutaro Yamada discussing their research on leveraging pre-trained language models for offline reinforcement learning. Delve into the experimental results, challenges, and insights gained from applying Wikipedia-trained models to control and game environments. Learn about the potential of transferring knowledge between generative modeling tasks across different domains, the impact on convergence speed and performance, and the implications for future research in reinforcement learning and sequence modeling. Gain valuable perspectives on model architectures, attention patterns, computational requirements, and practical advice for getting started in this emerging field.