Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing
Behavior cloning is enjoying a resurgence in popularity as scaling both model and data sizes proves to provide a strong starting point for many tasks of interest. In this work, we introduce an open recipe for training a video game playing foundation model designed for inference in realtime on a consumer GPU. We release all data (8300+ hours of high quality human gameplay), training and inference code, and pretrained checkpoints under an open license. We show that our best model is capable of playing a variety of 3D video games at a level competitive with human play. We use this recipe to systematically examine the scaling laws of behavior cloning to understand how the model's performance and causal reasoning varies with model and data scale. We first show in a simple toy problem that, for some types of causal reasoning, increasing both the amount of training data and the depth of the network results in the model learning a more causal policy. We then systematically study how causality varies with the number of parameters (and depth) and training steps in scaled models of up to 1.2 billion parameters, and we find similar scaling results to what we observe in the toy problem.
696062ce5b7998385e639442
Yuguang Yue
claimed_verified
2026-01-09T08:35:30.380Z
663a4918e2a8a70966782fac
/avatars/bd3ba99417688cf0c36920c3cb8b7309.svg
yuguang
guaguaa
user
guaguaa
696062ce5b7998385e639443
Irakli Salia
696062ce5b7998385e639444
Samuel Hunt
696062ce5b7998385e639445
Chris Green
696062ce5b7998385e639446
Wenzhe Shi
696062ce5b7998385e639447
Jonathan J Hunt
Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing
Behavior cloning is enjoying a resurgence in popularity as scaling both model and data sizes proves to provide a strong starting point for many tasks of interest. In this work, we introduce an open recipe for training a video game playing foundation model designed for inference in realtime on a consumer GPU. We release all data (8300+ hours of high quality human gameplay), training and inference code, and pretrained checkpoints under an open license. We show that our best model is capable of playing a variety of 3D video games at a level competitive with human play. We use this recipe to systematically examine the scaling laws of behavior cloning to understand how the model's performance and causal reasoning varies with model and data scale. We first show in a simple toy problem that, for some types of causal reasoning, increasing both the amount of training data and the depth of the network results in the model learning a more causal policy. We then systematically study how causality varies with the number of parameters (and depth) and training steps in scaled models of up to 1.2 billion parameters, and we find similar scaling results to what we observe in the toy problem.
Behavior cloning demonstrates improved performance and causal reasoning through scaling model size and training data, achieving human-level gameplay in 3D video games.
behavior cloning foundation model realtime inference human gameplay scaling laws causal reasoning model parameters training data video game playing