EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
For researchers studying agent learning from real-world environments, this work provides a foundational scaling law and a benchmark suite to accelerate progress.
This paper presents the first evidence that agent performance during environment learning follows a log-sigmoid scaling law with R²=0.998, based on 38,000 hours of interaction across 134 real-world tasks, and finds that agent learning speed roughly doubles every three months.
Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less understood. Analyzing roughly 38,000 hours of agent interaction with the environment across 134 real world tasks, we find, to the best of our knowledge, the first evidence that overall performance during environment learning follows a log-sigmoid scaling law with remarkably high precision, reaching R^2 = 0.998. Across model generations, we also find that agent learning speed roughly doubles every three months. This discovery stems from EdgeBench, a suite of 134 real world tasks with ultra-long horizons, spanning scientific discovery, software engineering, combinatorial optimization, professional knowledge work, formal mathematics, and interactive games. Each task sustains at least 12 hours of continuous agent operation under rich, multilevel feedback, and is built through substantial expert effort. We publicly release 51 tasks and our full evaluation framework to accelerate the study of how agents learn from real world experience.