Player-1: Learning game control from synthetic experience
Player-1 is a post-training framework for game-playing agents that aims to learn beyond demonstration data through synthetic experience. The authors first fine-tune computer-use models on 6,044 curated gameplay trajectories and extend the action space with relative pointer motion, sustained inputs and composite actions; they then run multi-environment PPO reinforcement learning in Player-1-Gym, a set of sixteen synthetic 2D and 3D environments with programmatically verifiable rewards and randomized observations and dynamics. The paper reports that Player-1-9B and Player-1-35B-A3B improve MineDojo-300 success over their respective SFT policies by 27.8 and 17.7 percentage points, with Player-1-9B reaching 38.33% versus 12.63% for the strongest evaluated baseline, plus further gains over SFT on ViZDoom-10 and GameWorld. It is an arXiv preprint submitted on 14 October 2026 (arXiv:2610.20584) with 12 authors.
It suggests that synthetic environments with verifiable rewards can fill gaps in demonstration coverage, offering a scalable training route for general digital-control agents.