This article presents Dynalang, an agent that connects language understanding with future predictions to improve task performance. Unlike traditional agents, Dynalang learns from both past and future language, enabling it to handle a variety of tasks more effectively. It can also be pretrained on text and video datasets without needing direct actions or rewards.

Large language models derive from decades of accessible text, but their data consumption outpaces human production, leading to a need for self-generated experiences in AI. The article discusses the importance of exploration in reinforcement learning and how better exploration can enhance generalization in models, highlighting the role of pretraining in solving exploration challenges. It emphasizes that the future of AI progress will focus more on collecting the right experiences rather than merely increasing model capacity.