diff --git a/tests/experimental/test_trainers_args.py b/tests/experimental/test_trainers_args.py
new file mode 100644
index 00000000000..bd86bb61b5d
--- /dev/null
+++ b/tests/experimental/test_trainers_args.py
@@ -0,0 +1,70 @@
+# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+from trl.experimental.bco import BCOConfig, BCOTrainer
+
+from ..testing_utils import TrlTestCase, require_sklearn
+
+
+class TestTrainerArg(TrlTestCase):
+    @require_sklearn
+    def test_bco(self):
+        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
+        training_args = BCOConfig(
+            self.tmp_dir,
+            max_length=256,
+            max_prompt_length=64,
+            max_completion_length=64,
+            beta=0.5,
+            label_pad_token_id=-99,
+            padding_value=-99,
+            truncation_mode="keep_start",
+            # generate_during_eval=True, # ignore this one, it requires wandb
+            is_encoder_decoder=True,
+            precompute_ref_log_probs=True,
+            model_init_kwargs={"trust_remote_code": True},
+            ref_model_init_kwargs={"trust_remote_code": True},
+            dataset_num_proc=4,
+            prompt_sample_size=512,
+            min_density_ratio=0.2,
+            max_density_ratio=20.0,
+        )
+        trainer = BCOTrainer(
+            model=model_id,
+            ref_model=model_id,
+            args=training_args,
+            train_dataset=dataset,
+            processing_class=tokenizer,
+        )
+        assert trainer.args.max_length == 256
+        assert trainer.args.max_prompt_length == 64
+        assert trainer.args.max_completion_length == 64
+        assert trainer.args.beta == 0.5
+        assert trainer.args.label_pad_token_id == -99
+        assert trainer.args.padding_value == -99
+        assert trainer.args.truncation_mode == "keep_start"
+        # self.assertEqual(trainer.args.generate_during_eval, True)
+        assert trainer.args.is_encoder_decoder
+        assert trainer.args.precompute_ref_log_probs
+        assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
+        assert trainer.args.ref_model_init_kwargs == {"trust_remote_code": True}
+        assert trainer.args.dataset_num_proc == 4
+        assert trainer.args.prompt_sample_size == 512
+        assert trainer.args.min_density_ratio == 0.2
+        assert trainer.args.max_density_ratio == 20.0
diff --git a/tests/test_trainers_args.py b/tests/test_trainers_args.py
index 2005b54337c..d48ae07ff48 100644
--- a/tests/test_trainers_args.py
+++ b/tests/test_trainers_args.py
@@ -16,8 +16,6 @@
 from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
 
 from trl import (
-    BCOConfig,
-    BCOTrainer,
     CPOConfig,
     CPOTrainer,
     DPOConfig,
@@ -39,58 +37,10 @@
     XPOTrainer,
 )
 
-from .testing_utils import TrlTestCase, require_sklearn
+from .testing_utils import TrlTestCase
 
 
 class TestTrainerArg(TrlTestCase):
-    @require_sklearn
-    def test_bco(self):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
-        training_args = BCOConfig(
-            self.tmp_dir,
-            max_length=256,
-            max_prompt_length=64,
-            max_completion_length=64,
-            beta=0.5,
-            label_pad_token_id=-99,
-            padding_value=-99,
-            truncation_mode="keep_start",
-            # generate_during_eval=True, # ignore this one, it requires wandb
-            is_encoder_decoder=True,
-            precompute_ref_log_probs=True,
-            model_init_kwargs={"trust_remote_code": True},
-            ref_model_init_kwargs={"trust_remote_code": True},
-            dataset_num_proc=4,
-            prompt_sample_size=512,
-            min_density_ratio=0.2,
-            max_density_ratio=20.0,
-        )
-        trainer = BCOTrainer(
-            model=model_id,
-            ref_model=model_id,
-            args=training_args,
-            train_dataset=dataset,
-            processing_class=tokenizer,
-        )
-        assert trainer.args.max_length == 256
-        assert trainer.args.max_prompt_length == 64
-        assert trainer.args.max_completion_length == 64
-        assert trainer.args.beta == 0.5
-        assert trainer.args.label_pad_token_id == -99
-        assert trainer.args.padding_value == -99
-        assert trainer.args.truncation_mode == "keep_start"
-        # self.assertEqual(trainer.args.generate_during_eval, True)
-        assert trainer.args.is_encoder_decoder
-        assert trainer.args.precompute_ref_log_probs
-        assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
-        assert trainer.args.ref_model_init_kwargs == {"trust_remote_code": True}
-        assert trainer.args.dataset_num_proc == 4
-        assert trainer.args.prompt_sample_size == 512
-        assert trainer.args.min_density_ratio == 0.2
-        assert trainer.args.max_density_ratio == 20.0
-
     def test_cpo(self):
         model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
         tokenizer = AutoTokenizer.from_pretrained(model_id)