diff --git a/crates/ml/src/cuda_pipeline/experience_kernels.cu b/crates/ml/src/cuda_pipeline/experience_kernels.cu
index 230e54a82..30f453b65 100644
--- a/crates/ml/src/cuda_pipeline/experience_kernels.cu
+++ b/crates/ml/src/cuda_pipeline/experience_kernels.cu
@@ -822,34 +822,32 @@ extern "C" __global__ void experience_action_select(
             q_max_d = fmaxf(q_max_d, qv);
             q_min_d = fminf(q_min_d, qv);
         }
-        /* Direction temperature: 2× Q-range ensures Short/Long always get ≥15%
-         * probability even when Flat Q is highest. Without this, Flat dominance
-         * grows to 90%+ as training progresses (zero-cost Flat has inherent Q advantage).
-         * 2× gives max ratio e^0.5:1 ≈ 1.65:1 between best and worst direction. */
-        float tau_d = fmaxf((q_max_d - q_min_d) * 2.0f, 0.01f);
-
-        float sum_e = 0.0f;
-        float exps_d[3]; /* b0_size is always 3 (Short/Flat/Long) */
-        for (int a = 0; a < b0_size; a++) {
-            float qv = __bfloat162float(q_b0[a]);
-            exps_d[a] = expf((qv - q_max_d) / tau_d);
-            sum_e += exps_d[a];
+        /* Direction selection: Flat floor + Boltzmann for directional.
+         *
+         * 30% of the time: force Flat (prevents over-trading on production).
+         * 70% of the time: Boltzmann over Short/Flat/Long proportional to Q.
+         * This guarantees ≥30% Flat regardless of Q-values or hold enforcement,
+         * without the Q-gap conviction filter's snowball dynamics. */
+        if (lcg_random(&rng) < 0.30f) {
+            dir_idx = 1; /* Flat floor: 30% unconditional */
+        } else {
+            /* Boltzmann over all 3 directions for remaining 70% */
+            float tau_d = fmaxf(q_max_d - q_min_d, 0.01f);
+            float sum_e = 0.0f;
+            float exps_d[3];
+            for (int a = 0; a < b0_size; a++) {
+                float qv = __bfloat162float(q_b0[a]);
+                exps_d[a] = expf((qv - q_max_d) / tau_d);
+                sum_e += exps_d[a];
+            }
+            float r = lcg_random(&rng) * sum_e;
+            float cum = 0.0f;
+            dir_idx = b0_size - 1;
+            for (int a = 0; a < b0_size; a++) {
+                cum += exps_d[a];
+                if (r < cum) { dir_idx = a; break; }
+            }
         }
-        float r = lcg_random(&rng) * sum_e;
-        float cum = 0.0f;
-        dir_idx = b0_size - 1;
-        for (int a = 0; a < b0_size; a++) {
-            cum += exps_d[a];
-            if (r < cum) { dir_idx = a; break; }
-        }
-
-        /* Q-gap conviction filter REMOVED from training path.
-         * Boltzmann already encodes conviction through softmax probabilities.
-         * The filter was redundant and harmful: after a high-Sharpe epoch, Bellman
-         * max bootstraps raise Q(Flat) towards Q(directional), shrinking the gap.
-         * The filter then overrides Boltzmann's directional samples → Flat grows
-         * → less directional experience → model loses directional learning signal.
-         * Conviction gating is preserved in the backtest evaluator (eps=0 path). */
     }
 
     /* Gem 3: Flat detection shortcut — when direction=Flat, magnitude is irrelevant.