diff --git a/crates/ml/src/trainers/dqn/smoke_tests/performance.rs b/crates/ml/src/trainers/dqn/smoke_tests/performance.rs
index 984c5d5d7..8313479a7 100644
--- a/crates/ml/src/trainers/dqn/smoke_tests/performance.rs
+++ b/crates/ml/src/trainers/dqn/smoke_tests/performance.rs
@@ -20,6 +20,12 @@ fn data_dir() -> String {
 }
 
 /// Measure end-to-end training throughput (3 epochs, real data).
+///
+/// Purpose: verify the training pipeline runs at non-trivial speed AND produces
+/// plausible metrics. Previously only checked `loss.is_finite()` which passes on
+/// trivial zeros or NaN-less garbage — giving no signal that anything healthy
+/// happened. We now assert a conservative steps/second floor and bound the
+/// Q-value magnitude.
 #[test]
 #[ignore] // Loads real training data — run via nightly CI or manual trigger
 fn test_training_throughput_measurement() -> anyhow::Result<()> {
@@ -34,16 +40,62 @@ fn test_training_throughput_measurement() -> anyhow::Result<()> {
     }))?;
     let elapsed = start.elapsed();
 
+    // ── loss: finite and non-negative ──
     assert_finite(metrics.loss, "loss");
+    assert!(
+        metrics.loss >= 0.0,
+        "loss should be non-negative (sign-bug indicator), got {}",
+        metrics.loss,
+    );
+
+    // ── epochs must have run ──
+    assert!(
+        metrics.epochs_trained >= 1,
+        "no epoch completed — throughput cannot be measured. metrics: {:?}",
+        metrics,
+    );
+
+    // ── throughput floor ──
+    // RTX 3050 Ti smoketest target: > 10 epochs/s is aspirational, > 0.05 epochs/s
+    // (i.e. each epoch < 20s) is a safe local floor.  Raise in CI if desired — the
+    // point of this floor is to catch catastrophic regressions (e.g. accidentally
+    // falling back to CPU, or a kernel that ran but pinned a CPU loop).
+    let epochs_per_sec = metrics.epochs_trained as f64 / elapsed.as_secs_f64().max(1e-6);
+    assert!(
+        epochs_per_sec > 0.05,
+        "Training throughput {:.3} epochs/s below floor 0.05 (laptop target). \
+         {} epochs in {:.2}s — CI may set a higher floor.",
+        epochs_per_sec,
+        metrics.epochs_trained,
+        elapsed.as_secs_f64(),
+    );
+
+    // ── avg_q_value must be present, finite, and bounded ──
+    // Rules out NaN-like explosions that happen to be finite-but-huge.
+    let avg_q = metrics
+        .additional_metrics
+        .get("avg_q_value")
+        .copied()
+        .unwrap_or(f64::NAN);
+    assert!(
+        avg_q.is_finite(),
+        "avg_q_value missing or NaN — diagnostic regressed: {:?}",
+        metrics.additional_metrics.get("avg_q_value"),
+    );
+    assert!(
+        avg_q.abs() < 1e6,
+        "avg_q_value {avg_q} is implausibly large (C51 atoms exhausted or \
+         NaN-like explosion). v_range is finite by construction.",
+    );
+
     info!(
         elapsed_secs = elapsed.as_secs_f64(),
         epochs_trained = metrics.epochs_trained,
+        epochs_per_sec = epochs_per_sec,
         "Training throughput"
     );
     info!(loss = metrics.loss, "Avg loss");
-    if let Some(&avg_q) = metrics.additional_metrics.get("avg_q_value") {
-        info!(avg_q_value = avg_q, "Avg Q-value");
-    }
+    info!(avg_q_value = avg_q, "Avg Q-value");
     if let Some(&final_eps) = metrics.additional_metrics.get("final_epsilon") {
         info!(final_epsilon = final_eps, "Final epsilon");
     }
@@ -121,6 +173,12 @@ fn test_per_sample_latency() -> anyhow::Result<()> {
 
 /// Single-epoch training on real data.
 /// Loads full 163K-bar dataset — too slow for CI (run via nightly or manual trigger).
+///
+/// Purpose: one epoch on real ES.FUT data produces valid training metrics.
+/// Previously asserted only `loss.is_finite()`, which passes on `loss == 0.0`
+/// (a sign-bug / accumulation-bug tell) or any bounded NaN-less value. A
+/// healthy epoch must complete at least one step, produce bounded non-negative
+/// loss, and a bounded finite avg_q_value.
 #[test]
 #[ignore]
 fn test_real_data_single_epoch() -> anyhow::Result<()> {
@@ -137,15 +195,56 @@ fn test_real_data_single_epoch() -> anyhow::Result<()> {
     }))?;
     let elapsed = start.elapsed();
 
+    // ── loss: finite, bounded, non-negative ──
+    // Negative loss would signal a sign bug (loss is ≥0 by construction — MSE + C51
+    // KL + CQL are all non-negative). Zero loss on a real DQN training run is also
+    // a tell: either the network is predicting exactly the targets (impossible at
+    // step 1) or the backward pass is returning nothing (accumulation bug). The
+    // upper bound 1e8 rules out NaN-propagation disguised as "finite".
     assert_finite(metrics.loss, "real_data_loss");
+    assert!(
+        metrics.loss >= 0.0,
+        "real_data loss is negative ({}) — sign bug in MSE / C51 / CQL accumulator",
+        metrics.loss,
+    );
+    assert!(
+        metrics.loss < 1e8,
+        "real_data loss {} exceeds bound 1e8 — finite but implausibly huge, check \
+         NaN propagation or reward scaling",
+        metrics.loss,
+    );
+
+    // ── at least one epoch completed ──
+    assert!(
+        metrics.epochs_trained >= 1,
+        "real_data single-epoch test produced 0 epochs. metrics: {:?}",
+        metrics,
+    );
+
+    // ── avg_q_value: finite + bounded ──
+    let avg_q = metrics
+        .additional_metrics
+        .get("avg_q_value")
+        .copied()
+        .unwrap_or(f64::NAN);
+    assert!(
+        avg_q.is_finite(),
+        "real_data avg_q_value missing/NaN: {:?}",
+        metrics.additional_metrics.get("avg_q_value"),
+    );
+    assert!(
+        avg_q.abs() < 1e6,
+        "real_data avg_q_value {avg_q} is implausibly large — C51 atoms exhausted \
+         or NaN-like explosion",
+    );
+
     info!(
         elapsed_secs = elapsed.as_secs_f64(),
         loss = metrics.loss,
+        epochs_trained = metrics.epochs_trained,
+        avg_q_value = avg_q,
         "Real-data single epoch"
     );
-    if let Some(&avg_q) = metrics.additional_metrics.get("avg_q_value") {
-        info!(avg_q_value = avg_q, "Avg Q-value");
-    }
     drop(trainer);
     drop(rt);
     Ok(())