diff --git a/docs/source/multi_gpu_training.mdx b/docs/source/multi_gpu_training.mdx
index 1e26e8806..03afc6a3a 100644
--- a/docs/source/multi_gpu_training.mdx
+++ b/docs/source/multi_gpu_training.mdx
@@ -92,9 +92,10 @@ For faster training, you can enable mixed precision (fp16 or bf16). This is conf
 ## Notes
 
 - The `--policy.use_amp` flag in `lerobot-train` is only used when **not** running with accelerate. When using accelerate, mixed precision is controlled by accelerate's configuration.
-- Training logs, checkpoints, and hub uploads are only done by the main process to avoid conflicts.
+- Training logs, checkpoints, and hub uploads are only done by the main process to avoid conflicts. Non-main processes have console logging disabled to prevent duplicate output.
 - The effective batch size is `batch_size × num_gpus`. If you use 4 GPUs with `--batch_size=8`, your effective batch size is 32.
 - Learning rate scheduling is handled correctly across multiple processes—LeRobot sets `step_scheduler_with_optimizer=False` to prevent accelerate from adjusting scheduler steps based on the number of processes.
 - When saving or pushing models, LeRobot automatically unwraps the model from accelerate's distributed wrapper to ensure compatibility.
+- WandB integration automatically initializes only on the main process, preventing multiple runs from being created.
 
 For more advanced configurations and troubleshooting, see the [Accelerate documentation](https://huggingface.co/docs/accelerate).
diff --git a/src/lerobot/utils/utils.py b/src/lerobot/utils/utils.py
index c01906bc9..18bb395b1 100644
--- a/src/lerobot/utils/utils.py
+++ b/src/lerobot/utils/utils.py
@@ -136,15 +136,18 @@ def init_logging(
     for handler in logger.handlers[:]:
         logger.removeHandler(handler)
 
-    # Write logs to console
-    console_handler = logging.StreamHandler()
-    console_handler.setFormatter(formatter)
-    console_handler.setLevel(console_level.upper())
-    logger.addHandler(console_handler)
-    if accelerator is not None and not accelerator.is_main_process:
-        # Disable duplicate logging on non-main processes
-        logging.info(f"Setting logging level on non-main process {accelerator.process_index} to WARNING.")
-        logging.getLogger().setLevel(logging.WARNING)
+    # Check if this is a non-main process in multi-GPU training
+    is_non_main_process = accelerator is not None and not accelerator.is_main_process
+
+    # Write logs to console (only for main process in multi-GPU training)
+    if not is_non_main_process:
+        console_handler = logging.StreamHandler()
+        console_handler.setFormatter(formatter)
+        console_handler.setLevel(console_level.upper())
+        logger.addHandler(console_handler)
+    else:
+        # For non-main processes, set logger level to WARNING to suppress most logs
+        logger.setLevel(logging.WARNING)
 
     # Additionally write logs to file
     if log_file is not None: