diff --git a/tests/experimental/test_trainers_args.py b/tests/experimental/test_trainers_args.py
deleted file mode 100644
index 09cb448b296..00000000000
--- a/tests/experimental/test_trainers_args.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# 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.
-
-import pytest
-from datasets import load_dataset
-from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
-
-from trl.experimental.bco import BCOConfig, BCOTrainer
-from trl.experimental.cpo import CPOConfig, CPOTrainer
-from trl.experimental.nash_md import NashMDConfig, NashMDTrainer
-from trl.experimental.xpo import XPOConfig, XPOTrainer
-
-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, "device_map": "auto", "dtype": "auto"}
-        assert trainer.args.ref_model_init_kwargs == {"trust_remote_code": True, "device_map": "auto", "dtype": "auto"}
-        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)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
-        training_args = CPOConfig(
-            self.tmp_dir,
-            max_length=256,
-            max_prompt_length=64,
-            max_completion_length=64,
-            beta=0.5,
-            label_smoothing=0.5,
-            loss_type="hinge",
-            disable_dropout=False,
-            cpo_alpha=0.5,
-            simpo_gamma=0.2,
-            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,
-            model_init_kwargs={"trust_remote_code": True},
-            dataset_num_proc=4,
-        )
-        trainer = CPOTrainer(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_smoothing == 0.5
-        assert trainer.args.loss_type == "hinge"
-        assert not trainer.args.disable_dropout
-        assert trainer.args.cpo_alpha == 0.5
-        assert trainer.args.simpo_gamma == 0.2
-        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.model_init_kwargs == {"trust_remote_code": True}
-        assert trainer.args.dataset_num_proc == 4
-
-    @pytest.mark.parametrize("mixtures_coef_list", [False, True])
-    def test_nash_md(self, mixtures_coef_list):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        model = AutoModelForCausalLM.from_pretrained(model_id)
-        ref_model = AutoModelForCausalLM.from_pretrained(model_id)
-        reward_model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
-        training_args = NashMDConfig(
-            self.tmp_dir,
-            mixture_coef=0.5 if not mixtures_coef_list else [0.5, 0.6],
-        )
-        trainer = NashMDTrainer(
-            args=training_args,
-            processing_class=tokenizer,
-            model=model,
-            ref_model=ref_model,
-            reward_funcs=reward_model,
-            train_dataset=dataset,
-        )
-        assert trainer.args.mixture_coef == (0.5 if not mixtures_coef_list else [0.5, 0.6])
-
-    @pytest.mark.parametrize("alpha_list", [False, True])
-    def test_xpo(self, alpha_list):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        model = AutoModelForCausalLM.from_pretrained(model_id)
-        ref_model = AutoModelForCausalLM.from_pretrained(model_id)
-        reward_model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
-        training_args = XPOConfig(
-            self.tmp_dir,
-            alpha=0.5 if not alpha_list else [0.5, 0.6],
-        )
-        trainer = XPOTrainer(
-            args=training_args,
-            processing_class=tokenizer,
-            model=model,
-            ref_model=ref_model,
-            reward_funcs=reward_model,
-            train_dataset=dataset,
-        )
-        assert trainer.args.alpha == (0.5 if not alpha_list else [0.5, 0.6])
diff --git a/tests/test_trainers_args.py b/tests/test_trainers_args.py
deleted file mode 100644
index 2097008b49b..00000000000
--- a/tests/test_trainers_args.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# 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.
-
-import pytest
-from datasets import load_dataset
-from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
-
-from trl import (
-    DPOConfig,
-    DPOTrainer,
-    FDivergenceType,
-    KTOConfig,
-    KTOTrainer,
-    OnlineDPOConfig,
-    OnlineDPOTrainer,
-    ORPOConfig,
-    ORPOTrainer,
-    RewardConfig,
-    RewardTrainer,
-    SFTConfig,
-    SFTTrainer,
-)
-
-from .testing_utils import TrlTestCase
-
-
-class TestTrainerArg(TrlTestCase):
-    def test_dpo(self):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
-        training_args = DPOConfig(
-            self.tmp_dir,
-            beta=0.5,
-            label_smoothing=0.5,
-            loss_type="hinge",
-            label_pad_token_id=-99,
-            pad_token=".",
-            truncation_mode="keep_start",
-            max_length=256,
-            max_prompt_length=64,
-            max_completion_length=64,
-            disable_dropout=False,
-            # generate_during_eval=True, # ignore this one, it requires wandb
-            precompute_ref_log_probs=True,
-            dataset_num_proc=4,
-            model_init_kwargs={"trust_remote_code": True},
-            ref_model_init_kwargs={"trust_remote_code": True},
-            model_adapter_name="dummy_adapter",
-            ref_adapter_name="dummy_adapter",
-            reference_free=True,
-            force_use_ref_model=True,
-            f_divergence_type="js_divergence",
-            f_alpha_divergence_coef=0.5,
-            # sync_ref_model=True, # cannot be True when precompute_ref_log_probs=True. Don't test this.
-            ref_model_mixup_alpha=0.5,
-            ref_model_sync_steps=32,
-            rpo_alpha=0.5,
-            discopop_tau=0.1,
-        )
-        trainer = DPOTrainer(
-            model=model_id,
-            ref_model=model_id,
-            args=training_args,
-            train_dataset=dataset,
-            processing_class=tokenizer,
-        )
-        assert trainer.args.beta == 0.5
-        assert trainer.args.label_smoothing == 0.5
-        assert trainer.args.loss_type == "hinge"
-        assert trainer.args.label_pad_token_id == -99
-        assert trainer.args.pad_token == "."
-        assert trainer.args.truncation_mode == "keep_start"
-        assert trainer.args.max_length == 256
-        assert trainer.args.max_prompt_length == 64
-        assert trainer.args.max_completion_length == 64
-        assert not trainer.args.disable_dropout
-        # self.assertEqual(trainer.args.generate_during_eval, True)
-        assert trainer.args.precompute_ref_log_probs
-        assert trainer.args.dataset_num_proc == 4
-        assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
-        assert trainer.args.ref_model_init_kwargs == {"trust_remote_code": True}
-        assert trainer.args.model_adapter_name == "dummy_adapter"
-        assert trainer.args.ref_adapter_name == "dummy_adapter"
-        assert trainer.args.reference_free
-        assert trainer.args.force_use_ref_model
-        assert trainer.args.f_divergence_type == FDivergenceType.JS_DIVERGENCE
-        assert trainer.args.f_alpha_divergence_coef == 0.5
-        # self.assertEqual(trainer.args.sync_ref_model, True)
-        assert trainer.args.ref_model_mixup_alpha == 0.5
-        assert trainer.args.ref_model_sync_steps == 32
-        assert trainer.args.rpo_alpha == 0.5
-        assert trainer.args.discopop_tau == 0.1
-
-    def test_kto(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 = KTOConfig(
-            self.tmp_dir,
-            max_length=256,
-            max_prompt_length=64,
-            max_completion_length=64,
-            beta=0.5,
-            desirable_weight=0.5,
-            undesirable_weight=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,
-        )
-        trainer = KTOTrainer(
-            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.desirable_weight == 0.5
-        assert trainer.args.undesirable_weight == 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
-
-    @pytest.mark.parametrize("beta_list", [False, True])
-    def test_online_dpo(self, beta_list):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        model = AutoModelForCausalLM.from_pretrained(model_id)
-        ref_model = AutoModelForCausalLM.from_pretrained(model_id)
-        reward_model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
-        training_args = OnlineDPOConfig(
-            self.tmp_dir,
-            max_new_tokens=42,
-            temperature=0.5,
-            missing_eos_penalty=0.33,
-            beta=0.6 if not beta_list else [0.6, 0.7],
-            loss_type="hinge",
-        )
-        trainer = OnlineDPOTrainer(
-            model=model,
-            ref_model=ref_model,
-            reward_funcs=reward_model,
-            args=training_args,
-            train_dataset=dataset,
-            processing_class=tokenizer,
-            reward_processing_classes=tokenizer,
-        )
-        assert trainer.args.max_new_tokens == 42
-        assert trainer.args.temperature == 0.5
-        assert trainer.args.missing_eos_penalty == 0.33
-        assert trainer.args.beta == (0.6 if not beta_list else [0.6, 0.7])
-        assert trainer.args.loss_type == "hinge"
-
-    def test_orpo(self):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
-        training_args = ORPOConfig(
-            self.tmp_dir,
-            max_length=256,
-            max_prompt_length=64,
-            max_completion_length=64,
-            beta=0.5,
-            disable_dropout=False,
-            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,
-            model_init_kwargs={"trust_remote_code": True},
-            dataset_num_proc=4,
-        )
-        trainer = ORPOTrainer(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 not trainer.args.disable_dropout
-        assert trainer.args.label_pad_token_id == -99
-
-    def test_reward(self):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        model = AutoModelForCausalLM.from_pretrained(model_id)
-        dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
-        training_args = RewardConfig(
-            self.tmp_dir,
-            max_length=256,
-            dataset_num_proc=4,
-            center_rewards_coefficient=0.1,
-        )
-        trainer = RewardTrainer(
-            model=model,
-            args=training_args,
-            train_dataset=dataset,
-            processing_class=tokenizer,
-        )
-        assert trainer.args.max_length == 256
-        assert trainer.args.dataset_num_proc == 4
-        assert trainer.args.center_rewards_coefficient == 0.1
-
-    def test_sft(self):
-        model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
-        dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
-        training_args = SFTConfig(
-            self.tmp_dir,
-            dataset_text_field="dummy_text_field",
-            packing=True,
-            max_length=256,
-            dataset_num_proc=4,
-            neftune_noise_alpha=0.1,
-            model_init_kwargs={"trust_remote_code": True},
-            dataset_kwargs={"append_concat_token": True, "skip_prepare_dataset": True},
-            eval_packing=True,
-        )
-        trainer = SFTTrainer(model_id, args=training_args, train_dataset=dataset)
-        assert trainer.args.dataset_text_field == "dummy_text_field"
-        assert trainer.args.packing
-        assert trainer.args.max_length == 256
-        assert trainer.args.dataset_num_proc == 4
-        assert trainer.args.neftune_noise_alpha == 0.1
-        assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
-        assert "append_concat_token" in trainer.args.dataset_kwargs
-        assert trainer.args.dataset_kwargs["append_concat_token"]
-        assert trainer.args.eval_packing