EdgeBench

EdgeBench finds log-sigmoid scaling laws for agent learning from real-world environments

A new arXiv preprint introduces EdgeBench, a suite of 134 real-world tasks with ultra-long horizons (12+ hours of continuous agent operation each) spanning scientific discovery, software engineering, formal mathematics, and other domains. Analyzing ~38,000 hours of agent-environment interaction, the authors report the first evidence that agent performance during environment learning follows a log-sigmoid scaling law with R²=0.998. They also find that agent learning speed roughly doubles every three months across model generations, drawing an analogy to pretraining scaling laws but for post-deployment environment learning. 51 tasks and the evaluation framework are publicly released.