DataSmith auto agent outperforms model architecture tweaks using 200x fewer tokens via data interventions
josh_wills · x · 2026-08-19
DatologyAI introduced DataSmith, an auto agent for data research, demonstrating that data quality is the ultimate compute multiplier.
Key Results:
- Performance: With only 734M post-training tokens (200x fewer than the default SmolLM3 recipe), DataSmith outperformed SmolLM3-3B's post-training performance by 3 percentage points.
- Superiority: It outperformed harnesses that also intervened on architecture and optimizers solely through data interventions.
Design Features:
DataSmith is designed to experiment substantially more than default harnesses, reason more, generate more research ideas, explore novel datasets, and spawn more subagents to find better data configurations.
Related event: DatologyAI Launches DataSmith, an Autonomous Data Research Agent(3 posts)→
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