Move GKDTrainer to experimental module

docs/source/_toctree.yml

@@ -60,8 +60,6 @@
       title: DPO
     - local: online_dpo_trainer
       title: Online DPO
-    - local: gkd_trainer
-      title: GKD
     - local: grpo_trainer
       title: GRPO
     - local: kto_trainer
@@ -107,6 +105,8 @@
     title: CPO
   - local: gfpo
     title: GFPO
+  - local: gkd_trainer
+    title: GKD
   - local: gold_trainer
     title: GOLD
   - local: grpo_with_replay_buffer

docs/source/dataset_formats.md

@@ -390,7 +390,7 @@ Choosing the right dataset type depends on the task you are working on and the s
 | [`experimental.bco.BCOTrainer`] | [Unpaired preference](#unpaired-preference) or [Preference (explicit prompt recommended)](#preference) |
 | [`experimental.cpo.CPOTrainer`] | [Preference (explicit prompt recommended)](#preference) |
 | [`DPOTrainer`] | [Preference (explicit prompt recommended)](#preference) |
-| [`GKDTrainer`] | [Prompt-completion](#prompt-completion) |
+| [`experimental.gkd.GKDTrainer`] | [Prompt-completion](#prompt-completion) |
 | [`GRPOTrainer`] | [Prompt-only](#prompt-only) |
 | [`KTOTrainer`] | [Unpaired preference](#unpaired-preference) or [Preference (explicit prompt recommended)](#preference) |
 | [`NashMDTrainer`] | [Prompt-only](#prompt-only) |

docs/source/example_overview.md

@@ -43,8 +43,8 @@ Scripts are maintained in the [`trl/scripts`](https://github.com/huggingface/trl
 | [`examples/scripts/cpo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/cpo.py) | This script shows how to use the [`experimental.cpo.CPOTrainer`] to fine-tune a model to increase helpfulness and harmlessness using the [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset. |
 | [`trl/scripts/dpo.py`](https://github.com/huggingface/trl/blob/main/trl/scripts/dpo.py) | This script shows how to use the [`DPOTrainer`] to fine-tune a model. |
 | [`examples/scripts/dpo_vlm.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo_vlm.py) | This script shows how to use the [`DPOTrainer`] to fine-tune a Vision Language Model to reduce hallucinations using the [openbmb/RLAIF-V-Dataset](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset) dataset. |
-| [`examples/scripts/evals/judge_tldr.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/evals/judge_tldr.py) | This script shows how to use [`HfPairwiseJudge`] or [`experimental.judges.OpenAIPairwiseJudge`] to judge model generations. |
-| [`examples/scripts/gkd.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/gkd.py) | This script shows how to use the [`GKDTrainer`] to fine-tune a model. |
+| [`examples/scripts/evals/judge_tldr.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/evals/judge_tldr.py) | This script shows how to use [`experimental.judges.HfPairwiseJudge`] or [`experimental.judges.OpenAIPairwiseJudge`] to judge model generations. |
+| [`examples/scripts/gkd.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/gkd.py) | This script shows how to use the [`experimental.gkd.GKDTrainer`] to fine-tune a model. |
 | [`trl/scripts/grpo.py`](https://github.com/huggingface/trl/blob/main/trl/scripts/grpo.py) | This script shows how to use the [`GRPOTrainer`] to fine-tune a model. |
 | [`examples/scripts/grpo_vlm.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/grpo_vlm.py) | This script shows how to use the [`GRPOTrainer`] to fine-tune a multimodal model for reasoning using the [lmms-lab/multimodal-open-r1-8k-verified](https://huggingface.co/datasets/lmms-lab/multimodal-open-r1-8k-verified) dataset. |
 | [`examples/scripts/gspo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/gspo.py) | This script shows how to use GSPO via the [`GRPOTrainer`] to fine-tune model for reasoning using the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. |

docs/source/gkd_trainer.md

@@ -19,10 +19,10 @@ This post-training method was contributed by [Kashif Rasul](https://huggingface.
 
 ## Usage tips
 
-The [`GKDTrainer`] is a wrapper around the [`SFTTrainer`] class that takes in a teacher model argument. It needs three parameters to be set via the [`GKDConfig`] namely:
+The [`experimental.gkd.GKDTrainer`] is a wrapper around the [`SFTTrainer`] class that takes in a teacher model argument. It needs three parameters to be set via the [`experimental.gkd.GKDConfig`] namely:
 
 * `lmbda`:  controls the student data fraction, i.e., the proportion of on-policy student-generated outputs. When `lmbda=0.0`, the loss reduces to supervised JSD where the student is trained with the token-level probabilities of the teacher. When `lmbda=1.0`, the loss reduces to on-policy JSD, where the student generates output sequences and token-specific feedback on these sequences from the teacher. For values in between [0, 1] it is random between the two based on the `lmbda` value for each batch.
-* `seq_kd`:  controls whether to perform Sequence-Level KD (can be viewed as supervised FT on teacher-generated out). When `seq_kd=True` and `lmbda=0.0`, the loss reduces to supervised JSD, where the teacher generates output sequences and the student receives token-specific feedback on these sequences from the teacher. 
+* `seq_kd`:  controls whether to perform Sequence-Level KD (can be viewed as supervised FT on teacher-generated out). When `seq_kd=True` and `lmbda=0.0`, the loss reduces to supervised JSD, where the teacher generates output sequences and the student receives token-specific feedback on these sequences from the teacher.
 * `beta`: controls the interpolation in the generalized Jensen-Shannon Divergence.  When `beta=0.0` the loss approximates forward KL divergence, while for `beta=1.0` the loss approximates reverse KL divergence. For values in between [0, 1] it interpolates between the two.
 
 The authors find that on-policy data (high `lmbda`) performs better and the optimal `beta` varied depending on the task and evaluation method.
@@ -34,11 +34,8 @@ The basic API is as follows:
 
 ```python
 from datasets import Dataset
-from trl import GKDConfig, GKDTrainer
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-)
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl.experimental.gkd import GKDConfig, GKDTrainer
 
 NUM_DUMMY_SAMPLES = 100
 
@@ -92,11 +89,11 @@ The dataset should be formatted as a list of "messages" where each message is a
 
 ## GKDTrainer
 
-[[autodoc]] GKDTrainer
+[[autodoc]] experimental.gkd.GKDTrainer
     - train
     - save_model
     - push_to_hub
 
 ## GKDConfig
 
-[[autodoc]] GKDConfig
+[[autodoc]] experimental.gkd.GKDConfig

docs/source/gold_trainer.md

@@ -13,7 +13,7 @@ Key capabilities:
 
 1. **Cross-tokenizer alignment** – GOLD incrementally decodes the student and teacher tokens, groups passages with the same visible text, and merges probabilities inside each group. This guarantees loss terms are computed over the full completion even when token boundaries differ.
 2. **Hybrid ULD loss** – when `uld_use_hybrid_loss` is enabled, GOLD compares exact vocabulary matches directly and falls back to the original sorted-probability ULD loss for unmatched tokens. This improves stability for students whose vocabularies only partially overlap with the teacher.
-3. **Seamless integration with GKD** – GOLD inherits the on-policy vs. off-policy scheduling from the [`GKDTrainer`](./gkd_trainer.md), so you can combine sequence-level KD, generalized JSD, and cross-tokenizer distillation in a single training run.
+3. **Seamless integration with GKD** – GOLD inherits the on-policy vs. off-policy scheduling from the [`experimental.gkd.GKDTrainer`], so you can combine sequence-level KD, generalized JSD, and cross-tokenizer distillation in a single training run.
 
 > [!NOTE]
 > GOLD is currently part of the `trl.experimental` namespace. APIs may change without notice while the feature is iterated on.
@@ -27,7 +27,7 @@ messages). Important configuration flags on [`GOLDConfig`] include:
 * `teacher_tokenizer_name_or_path` – required when `use_uld_loss=True`; GOLD uses the teacher tokenizer to align tokens.
 * `uld_use_hybrid_loss`, `uld_hybrid_matched_weight`, `uld_hybrid_unmatched_weight` – enables and weights the hybrid
   matched/unmatched loss.
-* `beta`, `lmbda`, `seq_kd` – inherited from `GKDConfig`, controlling the generalized JSD interpolation and on-policy
+* `beta`, `lmbda`, `seq_kd` – inherited from [`experimental.gkd.GKDConfig`], controlling the generalized JSD interpolation and on-policy
   sampling ratio.
 
 A minimal end-to-end example:

docs/source/index.md

@@ -48,7 +48,7 @@ Below is the current list of TRL trainers, organized by method type (⚡️ = vL
 
 ### Knowledge distillation
 
-- [`GKDTrainer`]
+- [`experimental.gkd.GKDTrainer`] 🧪
 - [`experimental.minillm.MiniLLMTrainer`] 🧪
 
 </div>

docs/source/liger_kernel_integration.md

@@ -67,7 +67,7 @@ training_args = KTOConfig(..., use_liger_kernel=True)
 <hfoption id="GKD">
 
 ```python
-from trl import GKDConfig
+from trl.experimental.gkd import GKDConfig
 
 training_args = GKDConfig(..., use_liger_kernel=True)
 ```

docs/source/paper_index.md

@@ -646,12 +646,12 @@ On-Policy Distillation has been shown to outperform SFT, GRPO and can be used to
 
 Additionally on-policy distillation is more compute efficient and is less prone to overfitting when trained with limited data.
 
-To train a model with on-policy distillation using TRL, you can use the following configuration, with the [`GKDTrainer`] and [`GKDConfig`]:
+To train a model with on-policy distillation using TRL, you can use the following configuration, with the [`experimental.gkd.GKDTrainer`] and [`experimental.gkd.GKDConfig`]:
 
 ```python
-from trl import GKDConfig
+from trl.experimental.gkd import GKDConfig
 
-config = GKDConfig(
+training_args = GKDConfig(
     lmbda=1.0, # student produces rollouts for all batches
     beta=1.0, # to ensure reverse-kl as the loss function
     teacher_model_name_or_path="teacher-model", # specify the teacher model

docs/source/reducing_memory_usage.md

@@ -165,7 +165,7 @@ training_args = KTOConfig(..., use_liger_kernel=True)
 <hfoption id="GKD">
 
 ```python
-from trl import GKDConfig
+from trl.experimental.gkd import GKDConfig
 
 training_args = GKDConfig(..., use_liger_kernel=True)
 ```

examples/scripts/gkd.py

@@ -58,8 +58,6 @@
 from transformers import AutoTokenizer, GenerationConfig
 
 from trl import (
-    GKDConfig,
-    GKDTrainer,
     LogCompletionsCallback,
     ModelConfig,
     ScriptArguments,
@@ -68,6 +66,7 @@
     get_peft_config,
     get_quantization_config,
 )
+from trl.experimental.gkd import GKDConfig, GKDTrainer
 
 
 # Enable logging in a Hugging Face Space

tests/test_gkd_trainer.py → tests/experimental/test_gkd_trainer.py

@@ -20,9 +20,9 @@
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 
-from trl import GKDConfig, GKDTrainer
+from trl.experimental.gkd import GKDConfig, GKDTrainer
 
-from .testing_utils import TrlTestCase, require_liger_kernel
+from ..testing_utils import TrlTestCase, require_liger_kernel
 
 
 class TestGKDTrainerGenerateOnPolicy(TrlTestCase):

trl/experimental/gkd/__init__.py

@@ -0,0 +1,19 @@
+# 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 .gkd_config import GKDConfig
+from .gkd_trainer import GKDTrainer
+
+
+__all__ = ["GKDConfig", "GKDTrainer"]

trl/experimental/gkd/gkd_config.py

@@ -0,0 +1,112 @@
+# 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 dataclasses import dataclass, field
+from typing import Any
+
+from transformers import TrainingArguments
+
+from ...trainer.sft_config import SFTConfig
+
+
+@dataclass
+class GKDConfig(SFTConfig):
+    """
+    Configuration class for [`experimental.gkd.GKDTrainer`].
+
+    This class includes only the parameters that are specific to GKD training. For a full list of training arguments,
+    please refer to the [`~transformers.TrainingArguments`] and [`SFTConfig`] documentation.
+
+    Args:
+        temperature (`float`, *optional*, defaults to `0.9`):
+            Temperature for sampling. The higher the temperature, the more random the completions.
+        lmbda (`float`, *optional*, defaults to `0.5`):
+            Lambda parameter that controls the student data fraction (i.e., the proportion of on-policy
+            student-generated outputs).
+        beta (`float`, *optional*, defaults to `0.5`):
+            Interpolation coefficient between `0.0` and `1.0` of the Generalized Jensen-Shannon Divergence loss. When
+            beta is `0.0`, the loss is the KL divergence. When beta is `1.0`, the loss is the Inverse KL Divergence.
+        max_new_tokens (`int`, *optional*, defaults to `128`):
+            Maximum number of tokens to generate per completion.
+        teacher_model_name_or_path (`str`, *optional*):
+            Model name or path of the teacher model. If `None`, the teacher model will be the same as the model being
+            trained.
+        teacher_model_init_kwargs (`dict[str, Any]]`, *optional*):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the teacher model
+            from a string.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model.
+        seq_kd (`bool`, *optional*, defaults to `False`):
+            Seq_kd parameter that controls whether to perform Sequence-Level KD (can be viewed as supervised FT on
+            teacher-generated output).
+    """
+
+    _VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["teacher_model_init_kwargs"]
+
+    temperature: float = field(
+        default=0.9,
+        metadata={"help": "Temperature for sampling. The higher the temperature, the more random the completions."},
+    )
+    lmbda: float = field(
+        default=0.5,
+        metadata={
+            "help": "Lambda parameter that controls the student data fraction (i.e., the proportion of on-policy "
+            "student-generated outputs)."
+        },
+    )
+    beta: float = field(
+        default=0.5,
+        metadata={
+            "help": "Interpolation coefficient between `0.0` and `1.0` of the Generalized Jensen-Shannon Divergence "
+            "loss. When beta is `0.0`, the loss is the KL divergence. When beta is `1.0`, the loss is the Inverse KL "
+            "Divergence."
+        },
+    )
+    max_new_tokens: int = field(
+        default=128,
+        metadata={"help": "Maximum number of tokens to generate per completion."},
+    )
+    teacher_model_name_or_path: str | None = field(
+        default=None,
+        metadata={
+            "help": "Model name or path of the teacher model. If `None`, the teacher model will be the same as the "
+            "model being trained."
+        },
+    )
+    teacher_model_init_kwargs: dict[str, Any] | None = field(
+        default=None,
+        metadata={
+            "help": "Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the "
+            "teacher model from a string."
+        },
+    )
+    disable_dropout: bool = field(
+        default=True,
+        metadata={"help": "Whether to disable dropouts in `model`."},
+    )
+    seq_kd: bool = field(
+        default=False,
+        metadata={
+            "help": "Seq_kd parameter that controls whether to perform Sequence-Level KD (can be viewed as supervised "
+            "FT on teacher-generated output)."
+        },
+    )
+
+    def __post_init__(self):
+        super().__post_init__()
+        # check lmbda and beta are in the range [0, 1]
+        if self.lmbda < 0.0 or self.lmbda > 1.0:
+            raise ValueError("lmbda must be in the range [0.0, 1.0].")
+        if self.beta < 0.0 or self.beta > 1.0:
+            raise ValueError("beta must be in the range [0.0, 1.0].")