What is the actual reusable unit of reasoning data?

The most useful move in this survey of 150+ post-training studies is a reframing of what reasoning data actually is. The field talks as if the asset being released is a dataset of prompt-response pairs. The primer argues the real reusable unit is a "verifier-bearing feedback interface" whose value is inseparable from six entangled factors: the verifier, the base model, the data lineage, the optimizer, the scaffold, and the inference budget. Change any one and the same "data" produces different gains. The central unresolved question therefore becomes attribution: when a model improves, which part of that interface changed?

This is the connective tissue under several findings the vault already holds separately. When does RL actually extend reasoning beyond pretraining? is exactly the base-model-and-lineage dependency the primer names — gains attributed to "data" are really data-times-headroom. Does RL teach reasoning or just when to use it? is the optimizer-and-scaffold dependency: the interface re-weights existing capability rather than installing new data content. And How do quality, diversity, and complexity affect synthetic data differently? is the construction half of the same problem — a dataset's effect cannot be read off its quality alone because the verifier and budget co-determine it.

The strongest counterargument is that "it's all entangled" can become an excuse for never isolating anything — a survey-level shrug. The primer's defense is that attribution is tractable if releases ship the interface, not just the pairs: report the verifier, the base, the optimizer, the budget, so gains become inspectable, comparable, and testable. For writing, the sharp claim is that the post-training literature's reproducibility crisis is a units problem — people are sharing the wrong object, and benchmark numbers without the interface are uninterpretable.