diff --git a/crates/ml/src/trainers/dqn/trainer/training_loop.rs b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
index 7a3d8bbaa..093f5defe 100644
--- a/crates/ml/src/trainers/dqn/trainer/training_loop.rs
+++ b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
@@ -3890,6 +3890,48 @@ impl DQNTrainer {
         //
         // Q-growth is still tracked via `prev_epoch_q_mean` in case future
         // diagnostics want it, but nothing mutates tau from this value.
+        //
+        // PRODUCTION SAFETY: Q-drift kill criterion.
+        //
+        // A model whose Q-values double every epoch is catastrophically unsafe
+        // for live trading — Kelly cap floats with Q-confidence, so a runaway
+        // Q drives oversized positions. Even one trading session with such a
+        // model can blow up a strategy. We must never deploy a model that
+        // exhibits this dynamic, so we halt training the moment we detect it.
+        //
+        // Criterion: if BOTH (a) |q_mean| has grown more than 2× since the
+        // previous epoch AND (b) |q_mean| is already past a 1.5 production-
+        // unsafe threshold, halt training and return error. The 2× threshold
+        // catches genuine geometric divergence (typical healthy growth is
+        // <30%/epoch); the 1.5 absolute floor prevents false positives in
+        // early training where small Q-magnitudes oscillate by large ratios
+        // (e.g. fold 0 ep4→ep5 shows 0.15→0.79 = 5.3× ratio but stays well
+        // inside production-safe range).
+        //
+        // Both thresholds are numerical-stability bounds (Invariant 1 carve-
+        // out) — not tuned hyperparameters. They're derived from the bug
+        // signature observed in train-multi-seed-p526h: F1 ep2 Q=+2.23 (2.7×
+        // jump from F1 ep1 Q=+0.82) was the first epoch where divergence was
+        // unambiguous.
+        //
+        // Skip on the first epoch of training (no prev_q to compare). DO NOT
+        // skip on fold-boundary epochs — fold-boundary divergence is
+        // precisely the failure mode this criterion is designed to catch.
+        if self.prev_epoch_q_mean.abs() > 1e-6 {
+            let curr_abs = q_mean.abs();
+            let prev_abs = self.prev_epoch_q_mean.abs();
+            let ratio = curr_abs / prev_abs;
+            if curr_abs > 1.5 && ratio > 2.0 {
+                return Err(anyhow::anyhow!(
+                    "Q-drift kill criterion triggered at epoch {}: \
+                     |q_mean| jumped from {:.4} to {:.4} (ratio {:.2}×). \
+                     A model with this dynamic is unsafe for production trading \
+                     (Kelly cap → oversized positions). Halting training; \
+                     model is rejected from deployment.",
+                    epoch + 1, self.prev_epoch_q_mean, q_mean, ratio,
+                ));
+            }
+        }
         self.prev_epoch_q_mean = q_mean;
 
         // Action diversity — per-branch from GPU monitoring (not re-derived through OrderRouter)
diff --git a/docs/dqn-wire-up-audit.md b/docs/dqn-wire-up-audit.md
index c43fba508..6030fe4b7 100644
--- a/docs/dqn-wire-up-audit.md
+++ b/docs/dqn-wire-up-audit.md
@@ -2,6 +2,44 @@
 
 **Status:** Populated during Plan 1 Task 6 (A.5 orphan audit). Updated on every commit per Invariant 7.
 
+Q-drift production-safety kill criterion (2026-04-28): The
+`train-multi-seed-p526h` repro (5ep×3fold, MSE-clamp + PopArt-carry
+landed) reproduced a geometric Q-divergence in fold 1 that we can no
+longer mistake for a measurement artefact. Trajectory:
+F0 ep5 Q=+0.79 → F1 ep1 Q=+0.82 (boundary handoff fine) → F1 ep2 Q=+2.23
+(2.7× jump) → F1 ep3 +4.05 → F1 ep4 +10.66 → F1 ep5 NaN at step 5.
+The MSE clamp (`299981f9b`) keeps loss reading honest at ~1.0-1.4
+throughout this divergence, so the train_loss is no longer hiding the
+real Q-drift. **Production stake**: 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. Per the user's
+"can't happen at production" philosophy, this commit installs a
+**production safety net** (not a root-cause fix) that hard-halts
+training the moment Q-drift is detected. Implementation in
+`training_loop.rs` immediately before the existing
+`self.prev_epoch_q_mean = q_mean` update: if (a) `|q_mean|` has grown
+more than 2× since previous epoch AND (b) `|q_mean| > 1.5` (production-
+unsafe absolute floor), return `Err` — training halts, model is
+rejected from deployment. 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 on the data: F0
+ep4→ep5 0.15→0.79 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 on the very first epoch (no
+prev_q to compare); does NOT skip on fold-boundary epochs — those
+are exactly the failures we're catching. **This is a safety net, not
+the fix.** A separate reset-gap audit (the actual root cause hunt) is
+queued to identify which fold-boundary state isn't being reset
+correctly. Suspect list per the inspection so far: `prev_grad_buf`
+(gradient vaccine reference), PopArt GPU Welford buffers
+(popart_mean/var/count — `popart_count` is huge from fold 0 making
+new-fold reward stats track too slowly), `isv_q_abs_ref_*` magnitude
+EMAs, spectral norm σ EMAs, TLOB Adam state. Touched: `training_loop.rs`
+(+30 LOC). cargo check clean at 13 warnings (workspace baseline). No
+fingerprint change.
+
 MSE warmup loss target clamp to C51 atom support (2026-04-28): the
 fold-boundary PopArt carry-forward + iqr floor (`d710f9d50`) reduced
 fold-1 epoch-1 train loss readings 16× (318k → 19k) but ep-2/ep-3