diff --git a/crates/ml/src/trainers/dqn/fused_training.rs b/crates/ml/src/trainers/dqn/fused_training.rs
index f45d62503..22ad1a259 100644
--- a/crates/ml/src/trainers/dqn/fused_training.rs
+++ b/crates/ml/src/trainers/dqn/fused_training.rs
@@ -701,14 +701,6 @@ impl FusedTrainingCtx {
 
         self.trainer.run_nan_checks_post_forward(self.batch_size)?;
 
-        // Per-phase sync on step 0 only — catch the hang immediately
-        if step == 0 {
-            eprintln!("H100_STEP0: forward done, syncing");
-            self.trainer.stream().synchronize()
-                .map_err(|e| anyhow::anyhow!("step0 post-forward sync: {e}"))?;
-            eprintln!("H100_STEP0: forward sync OK");
-        }
-
         // ── Step 2b: HER donor computation (outside graph_aux) ───────────
         // Donor indices vary per step (random/future/final). The computation
         // fills her.donor_indices GPU buffer. graph_aux captures the relabel
@@ -733,8 +725,6 @@ impl FusedTrainingCtx {
             }
         }
 
-        if step == 0 { eprintln!("H100_STEP0: HER done"); }
-
         // ── Step 3: Auxiliary ops (graph_aux captured or replayed) ────────
         if self.graph_aux.is_some() {
             self.pre_replay_state_update(agent);
@@ -744,13 +734,6 @@ impl FusedTrainingCtx {
             self.submit_aux_ops(agent, gpu_batch)?;
         }
 
-        if step == 0 {
-            eprintln!("H100_STEP0: aux done, syncing");
-            self.trainer.stream().synchronize()
-                .map_err(|e| anyhow::anyhow!("step0 post-aux sync: {e}"))?;
-            eprintln!("H100_STEP0: aux sync OK");
-        }
-
         // ── Step 4: Conditional ops (outside graph) ──────────────────────
 
         // Ensemble diversity (variable topology, complex forward passes).
@@ -759,13 +742,6 @@ impl FusedTrainingCtx {
                 tracing::warn!("Ensemble diversity step failed (non-fatal): {e}");
             }
         }
-        if step == 0 {
-            eprintln!("H100_STEP0: ensemble done, syncing");
-            self.trainer.stream().synchronize()
-                .map_err(|e| anyhow::anyhow!("step0 post-ensemble sync: {e}"))?;
-            eprintln!("H100_STEP0: ensemble sync OK");
-        }
-
         // Causal intervention.
         {
             let step = self.steps_since_varmap_sync;
@@ -773,13 +749,6 @@ impl FusedTrainingCtx {
                 tracing::warn!("Causal intervention failed (non-fatal): {e}");
             }
         }
-        if step == 0 {
-            eprintln!("H100_STEP0: causal done, syncing");
-            self.trainer.stream().synchronize()
-                .map_err(|e| anyhow::anyhow!("step0 post-causal sync: {e}"))?;
-            eprintln!("H100_STEP0: causal sync OK");
-        }
-
         // Gradient vaccine (1/10 steps).
         if self.steps_since_varmap_sync % 10 == 0 {
             if let Some(ref vaccine_batch) = self.pending_vaccine_batch {
@@ -792,13 +761,6 @@ impl FusedTrainingCtx {
         }
         self.pending_vaccine_batch = None;
 
-        if step == 0 {
-            eprintln!("H100_STEP0: vaccine done (conditional ops complete), syncing");
-            self.trainer.stream().synchronize()
-                .map_err(|e| anyhow::anyhow!("step0 post-conditional sync: {e}"))?;
-            eprintln!("H100_STEP0: conditional sync OK");
-        }
-
         // ── Step 5: Pruning + Adam ───────────────────────────────────────
         self.trainer.apply_pruning_mask()
             .map_err(|e| anyhow::anyhow!("Pruning mask apply: {e}"))?;
@@ -806,13 +768,6 @@ impl FusedTrainingCtx {
         let fused_result = self.trainer.replay_adam_and_readback()
             .map_err(|e| anyhow::anyhow!("graph_adam replay: {e}"))?;
 
-        if step == 0 {
-            eprintln!("H100_STEP0: adam done, syncing");
-            self.trainer.stream().synchronize()
-                .map_err(|e| anyhow::anyhow!("step0 post-adam sync: {e}"))?;
-            eprintln!("H100_STEP0: adam sync OK");
-        }
-
         // ── Step 6: PER priority update ─────────────────────────────────
         agent.update_priorities_from_td(
             self.trainer.td_errors_buf(),
@@ -820,13 +775,6 @@ impl FusedTrainingCtx {
             &self.stream,
         ).map_err(|e| anyhow::anyhow!("PER update: {e}"))?;
 
-        if step == 0 {
-            eprintln!("H100_STEP0: PER done, syncing");
-            self.trainer.stream().synchronize()
-                .map_err(|e| anyhow::anyhow!("step0 post-PER sync: {e}"))?;
-            eprintln!("H100_STEP0: ALL STEP 0 SYNCS PASSED");
-        }
-
         // Bookkeeping.
         agent.fused_post_step_gpu_bookkeeping()
             .map_err(|e| anyhow::anyhow!("Fused GPU bookkeeping: {e}"))?;
diff --git a/crates/ml/src/trainers/dqn/trainer/training_loop.rs b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
index 184220894..fbecdab13 100644
--- a/crates/ml/src/trainers/dqn/trainer/training_loop.rs
+++ b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
@@ -1174,7 +1174,6 @@ impl DQNTrainer {
             }
         }
 
-        eprintln!("H100_LOOP: starting {num_training_steps} training steps");
         for _step in 0..num_training_steps {
             let sample_start = std::time::Instant::now();
             let (batch, vaccine_batch) = {
@@ -1258,7 +1257,6 @@ impl DQNTrainer {
             }
         }
 
-        eprintln!("H100_LOOP: {} steps done, flushing readback", train_step_count);
         if let Some(ref mut fused) = self.fused_ctx {
             let _ = fused.flush_readback();
             if let Ok(stats) = fused.flush_q_stats_readback() {