Two Blind Spots in Auto-Research Loops: Statistical Significance and Post-Training Persistence
_TarunKathuria · x · 2026-10-07
Adding to a discussion on automated AI research, the author flags two gaps:
- Distinguishing statistically significant improvements from noise is hard, and most hill-climbing auto-research loops don't do proper evaluation.
- It's unclear how many pretraining gains persist after post-training — especially since the post-training stack (data, recipe) isn't well-defined as a model spec that auto-research loops can reason about.
Related event: Automated AI research loops stall on problem definition and evaluation(2 posts)→
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