AIBuildAI Agent autonomously optimizes a robot world model to SOTA-level performance in 29 hours (code & model open-sourced)
We are sharing a new demonstration of AI agents building AI models. Given only a task description, the RT-1 robot manipulation dataset, and an 8×A100 GPU node, AIBuildAI Agent autonomously completed the full model development lifecycle: understand the task → design approaches → write code → train models → analyze results → iterate and improve, with no human intervention.
Starting from NVIDIA’s open Cosmos-Predict2.5-2B world model, AIBuildAI Agent autonomously post-trained the model for robot trajectory-to-video prediction, improving performance from 17.75 to 25.56 PSNR and from 0.716 to 0.845 SSIM. The final model achieved:
🏆 SSIM: 0.845 — the highest among all published methods on the RT-1 benchmark;
📈 PSNR: 25.56 — ranked 4th among published results, within 0.5 dB of the best published trained model.
The entire process took only 29 hours.
During the run, AIBuildAI Agent explored multiple training strategies, dynamically allocated GPU resources, diagnosed and recovered from training failures, and integrated techniques discovered from recent research papers.
Training code and model are open-sourced: https://github.com/aibuildai/AI-Build-AI/tree/main/tasks/robot-world-model-post-training
Posted Aug 5, 2026, 1:25 PM UTC · locked