diff --git a/trl/trainer/sft_trainer.py b/trl/trainer/sft_trainer.py
index d101935301..11c08c1083 100644
--- a/trl/trainer/sft_trainer.py
+++ b/trl/trainer/sft_trainer.py
@@ -172,27 +172,28 @@ def __init__(
                 )
 
             if not isinstance(model, PeftModel):
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+                gradient_checkpointing_kwargs = getattr(args, "gradient_checkpointing_kwargs", None) or {}
                 if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
-                    _support_gc_kwargs = hasattr(
-                        args, "gradient_checkpointing_kwargs"
-                    ) and "gradient_checkpointing_kwargs" in list(
-                        inspect.signature(prepare_model_for_kbit_training).parameters
-                    )
-
                     preprare_model_kwargs = {
                         "use_gradient_checkpointing": getattr(args, "gradient_checkpointing", False)
                     }
 
                     if _support_gc_kwargs:
-                        preprare_model_kwargs["gradient_checkpointing_kwargs"] = getattr(
-                            args, "gradient_checkpointing_kwargs", None
-                        )
+                        preprare_model_kwargs["gradient_checkpointing_kwargs"] = gradient_checkpointing_kwargs
 
                     model = prepare_model_for_kbit_training(model, **preprare_model_kwargs)
 
                     if args is not None:
                         args = dataclasses.replace(args, gradient_checkpointing=False)
-                elif getattr(args, "gradient_checkpointing", False):
+                elif getattr(args, "gradient_checkpointing", False) and (
+                    "use_reentrant" not in gradient_checkpointing_kwargs
+                    or gradient_checkpointing_kwargs["use_reentrant"]
+                ):
                     # For backward compatibility with older versions of transformers
                     if hasattr(model, "enable_input_require_grads"):
                         model.enable_input_require_grads()