Apple Paper: Tool-Use Agents Gain More From Data Diversity Than More Tasks
rohanpaul_ai · x · 2026-10-29
Apple's paper 'Scaling Laws for Tool-Use Agents via Controllable Environment Synthesis' shows synthetic data mix, not just size, decides tool-use agent generalization. Their SynthAPI pipeline lets an LLM build synthetic apps with real Python tools and databases, then writes tasks with automatic success checks; across 849 trained models and 458,752 tasks, adding app and database diversity at a fixed 32,768-task budget lifted a 4B model on AppWorld's challenge split from 38.4% to 45.0%. Their mix-aware scaling law predicts held-out runs with 0.0285 error vs 0.1706 for a task-count-only law, and agents trained per its findings beat prior data recipes on four benchmarks.
Related event: Apple Paper Reveals Scaling Laws for Tool-Use Agents(3 posts)→
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