EdgeBench paper reveals log-sigmoid scaling laws for agent learning
rohanpaul_ai · x · 2026-08-29
The paper 'EdgeBench' analyzes 38,000 hours of agent interaction, finding that overall performance in environment learning follows a precise log-sigmoid scaling law (R² = 0.998). Learning speed roughly doubles every three months. EdgeBench covers 134 long-horizon tasks across domains like scientific discovery, software engineering, and math.
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