diff --git a/scripts/run_dpo.py b/scripts/run_dpo.py
index 06189674..fbd084e5 100644
--- a/scripts/run_dpo.py
+++ b/scripts/run_dpo.py
@@ -173,10 +173,7 @@ def main():
     ###############
     train_result = dpo_trainer.train()
     metrics = train_result.metrics
-    max_train_samples = (
-        data_args.max_train_samples if data_args.max_train_samples is not None else len(raw_datasets["train"])
-    )
-    metrics["train_samples"] = min(max_train_samples, len(raw_datasets["train"]))
+    metrics["train_samples"] = len(raw_datasets["train"])
     dpo_trainer.log_metrics("train", metrics)
     dpo_trainer.save_metrics("train", metrics)
     dpo_trainer.save_state()
@@ -189,10 +186,7 @@ def main():
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
         metrics = dpo_trainer.evaluate()
-        max_eval_samples = (
-            data_args.max_eval_samples if data_args.max_eval_samples is not None else len(raw_datasets["test"])
-        )
-        metrics["eval_samples"] = min(max_eval_samples, len(raw_datasets["test"]))
+        metrics["eval_samples"] = len(raw_datasets["test"])
         dpo_trainer.log_metrics("eval", metrics)
         dpo_trainer.save_metrics("eval", metrics)
 
diff --git a/scripts/run_sft.py b/scripts/run_sft.py
index 97cc0515..e0d892fc 100644
--- a/scripts/run_sft.py
+++ b/scripts/run_sft.py
@@ -151,8 +151,7 @@ def main():
     logger.info("*** Train ***")
     train_result = trainer.train()
     metrics = train_result.metrics
-    max_train_samples = data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
-    metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+    metrics["train_samples"] = len(train_dataset)
     trainer.log_metrics("train", metrics)
     trainer.save_metrics("train", metrics)
     trainer.save_state()
@@ -163,8 +162,7 @@ def main():
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
         metrics = trainer.evaluate()
-        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
-        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+        metrics["eval_samples"] = len(eval_dataset)
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
 
diff --git a/src/alignment/configs.py b/src/alignment/configs.py
index 9097d94d..9ca7c8e5 100644
--- a/src/alignment/configs.py
+++ b/src/alignment/configs.py
@@ -197,24 +197,6 @@ class DataArguments:
         default_factory=lambda: ["train", "test"],
         metadata={"help": ("List of train test splits to use in the dataset")},
     )
-    max_train_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of training examples to this "
-                "value if set."
-            )
-        },
-    )
-    max_eval_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
-                "value if set."
-            )
-        },
-    )
     preprocessing_num_workers: Optional[int] = field(
         default=None,
         metadata={"help": "The number of processes to use for the preprocessing."},