huggingface · qgallouedec · Aug 17, 2025 · Jul 26, 2025 · Jul 28, 2025 · Jul 28, 2025
diff --git a/docs/source/bco_trainer.md b/docs/source/bco_trainer.md
@@ -9,7 +9,7 @@ For a full example have a look at  [`examples/scripts/bco.py`].
 ## Expected dataset type
 
 The [`BCOTrainer`] requires an [unpaired preference dataset](dataset_formats#unpaired-preference).
-The [`BCOTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset format. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
+The [`BCOTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset formats. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
 
 ## Expected model format
 The BCO trainer expects a model of `AutoModelForCausalLM`, compared to PPO that expects `AutoModelForCausalLMWithValueHead` for the value function.

diff --git a/docs/source/cpo_trainer.md b/docs/source/cpo_trainer.md
diff --git a/docs/source/dataset_formats.md b/docs/source/dataset_formats.md
@@ -213,7 +213,7 @@ For more detailed information on tool calling, refer to the [Tool Calling sectio
 
 The [Harmony response format](https://cookbook.openai.com/articles/openai-harmony) was introduced with the [OpenAI GPT OSS models](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4). It extends the conversational format by adding richer structure for reasoning, function calls, and metadata about the model’s behavior. Key features include:
 
-- **Developer role** – Provides high-level instructions (similar to a system prompt) and lists available tools.
+- **Developer role** – Provides high level instructions (similar to a system prompt) and lists available tools.
 - **Channels** – Separate types of assistant output into distinct streams:
 
   - `analysis` – for internal reasoning, from the key `"thinking"`

diff --git a/docs/source/dpo_trainer.md b/docs/source/dpo_trainer.md
@@ -118,7 +118,7 @@ accelerate launch trl/scripts/dpo.py \
 
 ## Logged metrics
 
-While training and evaluating we record the following reward metrics:
+While training and evaluating, we record the following reward metrics:
 
 - `rewards/chosen`: the mean difference between the log probabilities of the policy model and the reference model for the chosen responses scaled by beta
 - `rewards/rejected`: the mean difference between the log probabilities of the policy model and the reference model for the rejected responses scaled by beta

diff --git a/docs/source/grpo_trainer.md b/docs/source/grpo_trainer.md
@@ -149,6 +149,8 @@ This constant is recommended to be the maximum completion length. To use this fo
 
 ## Logged metrics
 
+While training and evaluating, we record the following reward metrics:
+
 - `num_tokens`: The total number of tokens processed so far, including both prompts and completions.
 - `completions/mean_length`: The average length of generated completions.
 - `completions/min_length`: The minimum length of generated completions.

diff --git a/docs/source/kto_trainer.md b/docs/source/kto_trainer.md
@@ -74,7 +74,7 @@ Here are some other factors to consider when choosing a programming language for
 
 KTO requires an [unpaired preference dataset](dataset_formats#unpaired-preference). Alternatively, you can provide a *paired* preference dataset (also known simply as a *preference dataset*). In this case, the trainer will automatically convert it to an unpaired format by separating the chosen and rejected responses, assigning `label = True` to the chosen completions and `label = False` to the rejected ones.
 
-The [`KTOTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset format. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
+The [`KTOTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset formats. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
 
 In theory, the dataset should contain at least one chosen and one rejected completion. However, some users have successfully run KTO using *only* chosen or only rejected data. If using only rejected data, it is advisable to adopt a conservative learning rate.
 
@@ -118,7 +118,7 @@ By default, they are both 1. However, if you have more of one or the other, then
 
 ## Logged metrics
 
-While training and evaluating we record the following reward metrics:
+While training and evaluating, we record the following reward metrics:
 
 - `rewards/chosen_sum`: the sum of log probabilities of the policy model for the chosen responses scaled by beta
 - `rewards/rejected_sum`: the sum of log probabilities of the policy model for the rejected responses scaled by beta

diff --git a/docs/source/nash_md_trainer.md b/docs/source/nash_md_trainer.md
@@ -63,7 +63,7 @@ The best programming language depends on personal preference, the complexity of
 
 ## Expected dataset type
 
-Nash-MD requires a [prompt-only dataset](dataset_formats#prompt-only). The [`NashMDTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset format. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
+Nash-MD requires a [prompt-only dataset](dataset_formats#prompt-only). The [`NashMDTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset formats. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
 
 ## Usage tips
 
@@ -132,7 +132,7 @@ python examples/scripts/nash_md.py \
 
 ## Logged metrics
 
-The logged metrics are as follows:
+While training and evaluating, we record the following reward metrics:
 
 * `loss/kl`: The mean KL divergence between the model and reference data.
 * `objective/entropy`: The mean entropy of the model and reference data.

diff --git a/docs/source/online_dpo_trainer.md b/docs/source/online_dpo_trainer.md
@@ -65,7 +65,7 @@ The best programming language depends on your specific needs and priorities. Som
 
 ## Expected dataset type
 
-Online DPO only requires a [prompt-only dataset](dataset_formats#prompt-only) (unlike offline DPO, that expects [preference dataset](dataset_formats#preference)). The [`OnlineDPOTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset format. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
+Online DPO only requires a [prompt-only dataset](dataset_formats#prompt-only) (unlike offline DPO, that expects [preference dataset](dataset_formats#preference)). The [`OnlineDPOTrainer`] supports both [conversational](dataset_formats#conversational) and [standard](dataset_formats#standard) dataset formats. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
 
 ## Usage tips
 
@@ -132,7 +132,7 @@ python examples/scripts/dpo_online.py \
 
 ## Logged metrics
 
-The logged metrics are as follows. Here is an example [tracked run at Weights and Biases](https://wandb.ai/huggingface/trl/runs/w4apmsi9)
+While training and evaluating, we record the following reward metrics. Here is an example [tracked run at Weights and Biases](https://wandb.ai/huggingface/trl/runs/w4apmsi9)
 
 * `objective/kl`: The mean Kullback-Leibler (KL) divergence between the current model and reference model.
 * `objective/entropy`: The mean entropy of the model, indicating the randomness of the actions chosen by the model.

diff --git a/docs/source/orpo_trainer.md b/docs/source/orpo_trainer.md
@@ -109,7 +109,7 @@ To scale how much the auxiliary loss contributes to the total loss, use the hype
 
 ## Logged metrics
 
-While training and evaluating we record the following reward metrics:
+While training and evaluating, we record the following reward metrics:
 
 - `rewards/chosen`: the mean log probabilities of the policy model for the chosen responses scaled by beta
 - `rewards/rejected`: the mean log probabilities of the policy model for the rejected responses scaled by beta

diff --git a/docs/source/paper_index.md b/docs/source/paper_index.md
@@ -26,6 +26,26 @@ training_args = GRPOConfig(
 )
 ```
 
+## AlphaPO -- Reward shape matters for LLM alignment
+
+**📜 Paper**: https://huggingface.co/papers/2501.03884
+
+AlphaPO is a new Direct Alignment Algorithms (DAAs) method that leverages an alpha-parameter to help change the shape of the reward function beyond the standard log reward. AlphaPO helps maintain fine-grained control over likelihood displacement and over-optimization. To reproduce the paper's setting, use this configuration:
+
+```python
+from trl import CPOConfig
+
+# Mistral-Instruct from Table 3 of the paper
+training_args = CPOConfig(
+    loss_type="alphapo",
+    alpha=0.25,
+    beta=2.5,
+    simpo_gamma=0.1,
+    learning_rate=7e-7,
+    ...
+)
+```
+
 ## EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes
 
 **📜 Paper**: https://huggingface.co/papers/2508.00180

diff --git a/docs/source/sft_trainer.md b/docs/source/sft_trainer.md
@@ -112,6 +112,8 @@ Padding tokens (if present) are ignored in the loss computation by applying an i
 
 ## Logged metrics
 
+While training and evaluating we record the following reward metrics:
+
 * `global_step`: The total number of optimizer steps taken so far.
 * `epoch`: The current epoch number, based on dataset iteration.
 * `num_tokens`: The total number of tokens processed so far.

diff --git a/docs/source/xpo_trainer.md b/docs/source/xpo_trainer.md
@@ -131,7 +131,7 @@ python examples/scripts/xpo.py \
 
 ## Logged metrics
 
-The logged metrics are as follows:
+While training and evaluating we record the following reward metrics:
 
 * `loss/xpo`: The mean xpo part of the full loss.
 * `loss/dpo`: The mean dpo part of the full loss.

diff --git a/tests/test_cpo_trainer.py b/tests/test_cpo_trainer.py
@@ -153,10 +153,6 @@ def test_cpo_trainer_with_lora(self, config_name):
                     self.assertFalse(torch.equal(param, new_param))
 
     def test_compute_metrics(self):
-        model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
-        tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
-        tokenizer.pad_token = tokenizer.eos_token
-
         dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
 
         def dummy_compute_metrics(*args, **kwargs):
@@ -174,9 +170,9 @@ def dummy_compute_metrics(*args, **kwargs):
         )
 
         trainer = CPOTrainer(
-            model=model,
+            model=self.model,
             args=training_args,
-            processing_class=tokenizer,
+            processing_class=self.tokenizer,
             train_dataset=dummy_dataset["train"],
             eval_dataset=dummy_dataset["test"],
             compute_metrics=dummy_compute_metrics,
@@ -185,3 +181,40 @@ def dummy_compute_metrics(*args, **kwargs):
         trainer.train()
 
         self.assertEqual(trainer.state.log_history[-2]["eval_test"], 0.0)
+
+    def test_alphapo_trainer(self):
+        training_args = CPOConfig(
+            output_dir=self.tmp_dir,
+            per_device_train_batch_size=2,
+            max_steps=3,
+            remove_unused_columns=False,
+            gradient_accumulation_steps=1,
+            learning_rate=9e-1,
+            eval_strategy="steps",
+            beta=0.1,
+            loss_type="alphapo",
+            alpha=0.5,
+            simpo_gamma=0.5,
+            report_to="none",
+        )
+
+        dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
+
+        trainer = CPOTrainer(
+            model=self.model,
+            args=training_args,
+            processing_class=self.tokenizer,
+            train_dataset=dummy_dataset["train"],
+            eval_dataset=dummy_dataset["test"],
+        )
+
+        previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
+
+        trainer.train()
+
+        self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
+
+        for n, param in previous_trainable_params.items():
+            new_param = trainer.model.get_parameter(n)
+            if param.sum() != 0:
+                self.assertFalse(torch.equal(param, new_param))
diff --git a/trl/trainer/cpo_config.py b/trl/trainer/cpo_config.py
@@ -54,13 +54,20 @@ class CPOConfig(TrainingArguments):
                   [SLiC](https://huggingface.co/papers/2305.10425) paper.
                 - `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper.
                 - `"simpo"`: SimPO loss from the [SimPO](https://huggingface.co/papers/2405.14734) paper.
+                - `"alphapo"`: AlphaPO loss from the [AlphaPO](https://huggingface.co/papers/2501.03884) paper. This
+                  automatically sets `loss_type="simpo"` and `cpo_alpha=0.0`.
 
         disable_dropout (`bool`, *optional*, defaults to `True`):
             Whether to disable dropout in the model.
         cpo_alpha (`float`, *optional*, defaults to `1.0`):
             Weight of the BC regularizer in CPO training.
         simpo_gamma (`float`, *optional*, defaults to `0.5`):
             Target reward margin for the SimPO loss, used only when the `loss_type="simpo"`.
+        alpha (`float`, *optional*, defaults to `0.0`):
+            Alpha parameter that controls reward function shape across all loss types. When alpha=0 (default), uses
+            standard log probability rewards. When `alpha != 0`, applies AlphaPO transformation: `r = (1 - p^(-alpha))
+            / alpha` from the [AlphaPO paper](https://huggingface.co/papers/2501.03884). This parameter works with all
+            loss types.
         label_pad_token_id (`int`, *optional*, defaults to `-100`):
             Label pad token id. This argument is required if you want to use the default data collator.
         padding_value (`int` or `None`, *optional*, defaults to `None`):
@@ -142,7 +149,7 @@ class CPOConfig(TrainingArguments):
         default="sigmoid",
         metadata={
             "help": "Type of loss to use.",
-            "choices": ["sigmoid", "hinge", "ipo", "simpo"],
+            "choices": ["sigmoid", "hinge", "ipo", "simpo", "alphapo"],
         },
     )
     disable_dropout: bool = field(
@@ -157,6 +164,14 @@ class CPOConfig(TrainingArguments):
         default=0.5,
         metadata={"help": "Target reward margin for the SimPO loss, used only when the `loss_type='simpo'`."},
     )
+    alpha: float = field(
+        default=0.0,
+        metadata={
+            "help": "Alpha parameter that controls reward function shape across all loss types. When alpha=0 "
+            "(default), uses standard log probability rewards. When `alpha != 0`, applies AlphaPO transformation: "
+            "`r = (1 - p^(-alpha)) / alpha` from the AlphaPO paper. This parameter works with all loss types."
+        },
+    )
     label_pad_token_id: int = field(
         default=-100,
         metadata={"help": "Label pad token id."},
@@ -195,4 +210,9 @@ class CPOConfig(TrainingArguments):
     def __post_init__(self):
         self.bf16 = not (self.fp16) if self.bf16 is None else self.bf16
 
+        # Syntactic sugar for AlphaPO: set loss_type to "simpo" and cpo_alpha to 0.0
+        if self.loss_type == "alphapo":
+            self.loss_type = "simpo"
+            self.cpo_alpha = 0.0
+
         super().__post_init__()
diff --git a/trl/trainer/cpo_trainer.py b/trl/trainer/cpo_trainer.py
@@ -319,6 +319,9 @@ def make_inputs_require_grad(module, input, output):
         if args.loss_type == "simpo":
             self.simpo_gamma = args.simpo_gamma
 
+        # AlphaPO parameter for reward shaping
+        self.alpha = args.alpha
+
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
 
         # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
@@ -659,7 +662,20 @@ def cpo_loss(
             loss for each example in the batch. The chosen_rewards and rejected_rewards tensors contain the rewards for
             the chosen and rejected responses, respectively.
         """
-        logits = (policy_chosen_logps - policy_rejected_logps).to(self.accelerator.device)
+        # Apply AlphaPO reward transformation if alpha != 0
+        if self.alpha != 0.0:
+            # Compute probabilities
+            chosen_probs = torch.exp(policy_chosen_logps)
+            rejected_probs = torch.exp(policy_rejected_logps)
+
+            # Apply AlphaPO transformation: r = (1 - p^(-alpha)) / alpha
+            policy_chosen_rewards = (1 - chosen_probs.pow(-self.alpha)) / self.alpha
+            policy_rejected_rewards = (1 - rejected_probs.pow(-self.alpha)) / self.alpha
+
+            logits = (policy_chosen_rewards - policy_rejected_rewards).to(self.accelerator.device)
+        else:
+            # Standard log probability rewards when alpha = 0
+            logits = (policy_chosen_logps - policy_rejected_logps).to(self.accelerator.device)
 
         # The beta is a temperature parameter for the CPO loss, typically something in the range of 0.1 to 0.5.
         # We ignore the reference model as beta -> 0. The label_smoothing parameter encodes our uncertainty about the labels and
@@ -689,8 +705,15 @@ def cpo_loss(
                 f"Unknown loss type: {self.loss_type}. Should be one of ['sigmoid', 'hinge', 'ipo', 'simpo']"
             )
 
-        chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach()
-        rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach()
+        # Calculate rewards for logging
+        if self.alpha != 0.0:
+            # When using AlphaPO transformation, use the transformed rewards
+            chosen_rewards = self.beta * policy_chosen_rewards.to(self.accelerator.device).detach()
+            rejected_rewards = self.beta * policy_rejected_rewards.to(self.accelerator.device).detach()
+        else:
+            # Standard log probability rewards
+            chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach()
+            rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach()
 
         return losses, chosen_rewards, rejected_rewards