diff --git a/studio/backend/core/training/trainer.py b/studio/backend/core/training/trainer.py
index 330fb74747..0aa6d0922e 100644
--- a/studio/backend/core/training/trainer.py
+++ b/studio/backend/core/training/trainer.py
@@ -3270,25 +3270,25 @@ class UnslothTrainer:
                     logger.info(f"Post-filter dataset size: {filtered_len} samples\n")
 
                     # [DEBUG] Decode first sample AFTER train_on_completions applied
-                    try:
-                        _row = self.trainer.train_dataset[0]
-                        _space = self.tokenizer(
-                            " ", add_special_tokens = False
-                        ).input_ids[0]
-                        print("[DEBUG] === After train_on_completions ===", flush = True)
-                        print(
-                            f"[DEBUG] input_ids decoded:\n{self.tokenizer.decode(_row['input_ids'])}\n",
-                            flush = True,
-                        )
-                        print(
-                            f"[DEBUG] labels decoded (-100 → space):\n{self.tokenizer.decode([_space if x == -100 else x for x in _row['labels']])}\n",
-                            flush = True,
-                        )
-                    except Exception as _dbg_e:
-                        print(
-                            f"[DEBUG] Could not decode post-completions sample: {_dbg_e}",
-                            flush = True,
-                        )
+                    # try:
+                    #     _row = self.trainer.train_dataset[0]
+                    #     _space = self.tokenizer(
+                    #         " ", add_special_tokens = False
+                    #     ).input_ids[0]
+                    #     print("[DEBUG] === After train_on_completions ===", flush = True)
+                    #     print(
+                    #         f"[DEBUG] input_ids decoded:\n{self.tokenizer.decode(_row['input_ids'])}\n",
+                    #         flush = True,
+                    #     )
+                    #     print(
+                    #         f"[DEBUG] labels decoded (-100 → space):\n{self.tokenizer.decode([_space if x == -100 else x for x in _row['labels']])}\n",
+                    #         flush = True,
+                    #     )
+                    # except Exception as _dbg_e:
+                    #     print(
+                    #         f"[DEBUG] Could not decode post-completions sample: {_dbg_e}",
+                    #         flush = True,
+                    #     )
 
                 except Exception as e:
                     logger.warning(f"Failed to apply train on responses only: {e}")
diff --git a/studio/backend/core/training/worker.py b/studio/backend/core/training/worker.py
index c2801227ff..685a4a674e 100644
--- a/studio/backend/core/training/worker.py
+++ b/studio/backend/core/training/worker.py
@@ -314,21 +314,21 @@ def run_training_process(
         # [DEBUG] Print first sample before model is loaded
         # dataset is a dict {"dataset": <Dataset>, "detected_format": ..., ...}
         # or a raw Dataset for audio paths
-        try:
-            ds = dataset["dataset"] if isinstance(dataset, dict) else dataset
-            print(
-                f"\n[DEBUG] Dataset loaded BEFORE model. type={type(ds).__name__}, len={len(ds)}",
-                flush = True,
-            )
-            print(f"[DEBUG] Columns: {ds.column_names}", flush = True)
-            sample = ds[0]
-            preview = {k: str(v)[:300] for k, v in sample.items()}
-            print(f"[DEBUG] First sample: {preview}\n", flush = True)
-        except Exception as e:
-            print(
-                f"[DEBUG] Could not preview first sample: {type(e).__name__}: {e}",
-                flush = True,
-            )
+        # try:
+        #     ds = dataset["dataset"] if isinstance(dataset, dict) else dataset
+        #     print(
+        #         f"\n[DEBUG] Dataset loaded BEFORE model. type={type(ds).__name__}, len={len(ds)}",
+        #         flush = True,
+        #     )
+        #     print(f"[DEBUG] Columns: {ds.column_names}", flush = True)
+        #     sample = ds[0]
+        #     preview = {k: str(v)[:300] for k, v in sample.items()}
+        #     print(f"[DEBUG] First sample: {preview}\n", flush = True)
+        # except Exception as e:
+        #     print(
+        #         f"[DEBUG] Could not preview first sample: {type(e).__name__}: {e}",
+        #         flush = True,
+        #     )
 
         # Disable eval if eval_steps <= 0
         eval_steps = config.get("eval_steps", 0.00)