Real Data Distributions Are Rewriting Training Experiences
chris_j_paxton · x · 2026-07-17
The author believes one of the biggest lessons this year is that many past methods relying on fixed priors and heavily constrained data pipelines might need to be overturned.
They quote the view that experiences learned in low-data, easily overfitted scenarios cannot be directly transferred to high-data phases. Last year, relying on strong inductive biases and extremely strict data collection resulted in a "functional but fragile" policy. This year, relaxing constraints to let data cover real-world state distributions led to entirely new, unpredicted capabilities.
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