diff --git a/studio/backend/core/training/worker.py b/studio/backend/core/training/worker.py
index e386a4f853..a2cf29536c 100644
--- a/studio/backend/core/training/worker.py
+++ b/studio/backend/core/training/worker.py
@@ -230,12 +230,12 @@ def run_training_process(
         # [DEBUG] Print first sample before model is loaded
         try:
             sample = dataset[0] if len(dataset) > 0 else None
-            logger.info(f"[DEBUG] Dataset loaded BEFORE model. First sample keys: {list(sample.keys()) if sample else 'empty'}")
+            print(f"\n[DEBUG] Dataset loaded BEFORE model. First sample keys: {list(sample.keys()) if sample else 'empty'}", flush=True)
             if sample:
                 preview = {k: str(v)[:200] for k, v in sample.items()}
-                logger.info(f"[DEBUG] First sample preview: {preview}")
+                print(f"[DEBUG] First sample preview: {preview}\n", flush=True)
         except Exception as e:
-            logger.info(f"[DEBUG] Could not preview first sample: {e}")
+            print(f"[DEBUG] Could not preview first sample: {e}", flush=True)
 
         # Disable eval if eval_steps <= 0
         eval_steps = config.get("eval_steps", 0.00)
diff --git a/studio/backend/utils/datasets/vlm_processing.py b/studio/backend/utils/datasets/vlm_processing.py
index 4c7e86c38f..4caf80cbbb 100644
--- a/studio/backend/utils/datasets/vlm_processing.py
+++ b/studio/backend/utils/datasets/vlm_processing.py
@@ -200,10 +200,10 @@ def generate_smart_vlm_instruction(
             dataset_name=dataset_name,
         )
         if llm_result and llm_result.get("instruction"):
-            import logging
-            logging.getLogger(__name__).info(
-                f"[DEBUG] LLM-assisted VLM instruction generated: "
-                f"'{llm_result['instruction']}' (confidence={llm_result.get('confidence', 'N/A')})"
+            print(
+                f"\n[DEBUG] LLM-assisted VLM instruction generated: "
+                f"'{llm_result['instruction']}' (confidence={llm_result.get('confidence', 'N/A')})\n",
+                flush=True,
             )
             return {
                 "instruction": llm_result["instruction"],