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EdgeBench analyzes 38,000 hours of real-world agent interactions across 134 tasks, revealing log-sigmoid scaling laws for performance and exponential learning speed improvements. The paper introduces a benchmark suite for studying how agents learn from real-world experience.
The ByteDance Seed team released the EdgeBench benchmark, which allows AI models to work continuously for 12-72 hours to evaluate their learning ability in long-horizon tasks. They discovered that the relationship between the time spent learning from the environment and performance follows a log-sigmoid curve, revealing a new scaling law.
ByteDance Seed released EdgeBench, a benchmark that tests whether AI agents can improve through experience by performing real-world tasks over 12+ hours, shifting evaluation from static knowledge to dynamic learning.
EdgeBench reveals a new scaling law indicating that on-the-fly AI learning speed doubles every three months.