Scaling Laws for Pre-training Agents and World Models

Tim Pearce, Tabish Rashid, David Bignell, Raluca Georgescu, Sam Devlin, Katja Hofmann

Keywords: world modeling, imitation learning, scaling laws

Abstract: The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video games, when simple learning objectives on offline datasets (pre-training) are used to model an agent's behavior (imitation learning) or their environment (world modeling). This paper characterizes the role of scale in these tasks more precisely. Going beyond the simple intuition that `bigger is better', we show that the same types of power laws found in language modeling (e.g. between loss and optimal model size), also arise in world modeling and imitation learning. However, the coefficients of these laws are influenced by the tokenizer, task \& architecture -- this has important implications on optimal sizing of models and data.

Primary Area: generative models

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Submission Number: 9184