craft-validation-masks-capacity-inversion

IN derived (depth 9)

Created 2026-06-21T11:40:20+00:00 · Reviewed 2026-06-21T14:41:08+00:00

The craft discipline's empirical validation methodology actively masks the fundamental capacity inversion between pretraining and alignment: because the field validates by deployment outcomes rather than formal analysis, the training pipeline's standardized stages appear uniformly mature even though pretraining benefits from parameter redundancy while alignment faces strict capacity constraints requiring entirely different scaling strategies.

Justifications

SL — craft epistemics (validate by outcome) cannot detect the internal asymmetry between pipeline stages

Antecedents (all must be IN):

  • IN training-pipeline-masks-fundamental-capacity-inversion — The mature training pipeline's standardized stages mask a fundamental capacity inversion: pretraining benefits from parameter redundancy (over-parameterized models remain compressible), while alignment is bottlenecked by reward model capacity (scaling the reward model matters more than scaling data), and this asymmetry is hidden by the pipeline's apparent end-to-end reproducibility.
  • IN llm-field-is-fundamentally-craft-discipline — The LLM field is fundamentally a craft discipline: both its most valuable structural properties (cross-boundary innovation, parameter redundancy) and its deepest barriers (tacit deployment knowledge, experiential prerequisites) are discovered and transmitted empirically, not through formal theory — meaning neither mastery nor failure modes are accessible through documentation alone.

Dependents

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