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Every rung gets written stop criteria - hit any one, stop; tightened when priors say results should come fast

A lesson the Coach cites as preregistered-stop-criteria-per-rung.
ReplicatedTraining runs

Freeze per-rung stop criteria (old-skill floors, new-skill deadline, oscillation signature, known pathology signatures) before training, stop on first hit - and shorten the deadline in proportion to how fast your mechanism says results should appear.

Symptom

Continued training past the point of degradation had previously destroyed capabilities (C1 trained away the root's backward skill); without hard stop rules, sunk-cost reasoning keeps runs alive too long.

Context

Standard rung protocol - eval_c_matrix every 100 iters at 5 seeds with a fixed criteria list, frozen before training ("开训前写死,事后不许改"): (1) stand or vx+0.15 at <=3/5 for two consecutive points; (2) vx+0.15 tracking <70% for two consecutive points (vs recorded root baseline); (3) oscillation signature - survival repeatedly crossing zero between adjacent checkpoints, stop on first occurrence, do not wait for confirmation; (4) new-skill-no-progress deadline (e.g. wz tracking <30% or still same-signed after iter 400); (5) a known lineage pathology signature (s1c arm-B: scatter -> half-recover -> collapse).

Change

Stop budgets scale with prior knowledge: when C4-redo4's feed-forward was already proven open-loop, the no-progress deadline was tightened from +500 to +100 iters ("前馈是开环就给 71~96% 的 … 若 +100 还没有 … 不值得再烧").

Outcome

Multiple rungs were stopped exactly on criterion (C4-redo criterion 4 at iter 1200; redo2/redo3 at absolute 900), converting each into a clean hypothesis test instead of a drifting run; no rung burned its full cap on a dead hypothesis.

Mechanism

Degradation of retained skills is a lineage-level injury that later training often cannot undo, so detection latency is capital loss; and a stop rule written before the run cannot be bent by hope. Tightening deadlines when priors predict immediate results converts "no result yet" into evidence against the mechanism.

Applies when

  • starting any resumed/curriculum training rung
  • deciding whether to keep training a run that shows early regression
  • a mechanism-backed change should produce results immediately
“每 100 iter eval_c_matrix.py --seeds 5,命中任一即停 … 存活在相邻 checkpoint 间反复跨越 0(出现即停)… 收紧版:绝对 iter 900(= +200)时 vy±0.10 跟踪仍 <30% 即停”
train/C_LADDER_RUN.md § 3h. 预注册停梯判据(开训前写死,事后不许改)

Principles that cite it