The train-multi-seed-p526h repro (with MSE-clamp + PopArt-carry already
landed) reproduced a geometric Q-divergence in fold 1 that train_loss
can no longer hide:
F0 ep5 Q=+0.79 (healthy carryover)
F1 ep1 Q=+0.82 (boundary handoff fine)
F1 ep2 Q=+2.23 (2.7× jump — drift starts)
F1 ep3 Q=+4.05
F1 ep4 Q=+10.66
F1 ep5 NaN at step 5
A model with this Q dynamic is catastrophically unsafe for live trading:
Kelly cap floats with Q-confidence, so runaway Q drives oversized
positions and can blow up the strategy before any downstream safety
trips. This commit installs a production safety net — NOT a root-cause
fix — that hard-halts training the moment Q-drift is detected:
if |q_mean| > 2× prev AND |q_mean| > 1.5 production-unsafe floor
→ return Err, training halts, model rejected from deployment
Inserted in training_loop.rs immediately before the existing
`self.prev_epoch_q_mean = q_mean` update so the comparison uses the
same source-of-truth values that already feed downstream diagnostics.
The 2× ratio catches genuine geometric divergence (typical healthy
growth <30%/epoch); the 1.5 absolute floor prevents false positives
in early training where small Q magnitudes oscillate by large ratios
(verified: F0 ep4→ep5 ratio 5.3× would NOT trigger because |0.79|<1.5).
Both thresholds are numerical-stability bounds (Invariant 1 carve-
out), not tuned hyperparameters. Skips first epoch (no prev_q). Does
NOT skip fold-boundary epochs — those are exactly the failures
we're catching.
This is a safety net. The actual root cause is a fold-boundary reset
gap (which reset isn't firing correctly?). A systematic audit is
queued to identify it. Suspects: prev_grad_buf, PopArt GPU Welford
buffers (popart_count huge from fold 0 → new-fold stats track too
slowly), isv_q_abs_ref_* magnitude EMAs, spectral norm σ EMAs, TLOB
Adam state.
Per user's "can't happen at production" philosophy: production
deployments must have this safety net regardless of whether the
root-cause fix lands first.
1c917e3cb0