diff --git a/crates/ml/src/trainers/dqn/trainer/training_loop.rs b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
index 3de566dfb..a5d43089f 100644
--- a/crates/ml/src/trainers/dqn/trainer/training_loop.rs
+++ b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
@@ -2457,7 +2457,12 @@ impl DQNTrainer {
         }
 
         // 2. Save checkpoint when improving
-        let prev_sharpe = self.sharpe_history.last().copied().unwrap_or(0.0) as f32;
+        // Use second-to-last sharpe (last is current epoch, already pushed before this runs)
+        let prev_sharpe = if self.sharpe_history.len() >= 2 {
+            self.sharpe_history[self.sharpe_history.len() - 2] as f32
+        } else {
+            0.0
+        };
         let improvement_rate = val_sharpe_f32 - prev_sharpe; // per epoch
         if improvement_rate > 0.01 && !self.backtracking.route_active {
             self.save_backtracking_checkpoint(epoch, improvement_rate, val_sharpe_f32)?;
@@ -2522,10 +2527,10 @@ impl DQNTrainer {
         // reset (zeroes m/v for indices 8-41, preserves trunk momentum). This may break
         // Adam fixed point without changing weights. Give it 3 epochs to take effect.
         // Full rewind triggers at plateau_threshold + 3.
+        // Branch Adam reset doesn't need checkpoints — it's a lightweight in-place fix.
         if self.backtracking.plateau_epochs == self.backtracking.plateau_threshold
             && !self.backtracking.route_active
             && epoch.saturating_sub(self.backtracking.last_rewind_epoch) >= self.backtracking.min_rewind_interval
-            && !self.backtracking.checkpoints.is_empty()
         {
             if let Some(ref mut fused) = self.fused_ctx {
                 if let Err(e) = fused.reset_branch_adam_momentum() {