---
title: "RLHF (Beta)"
description: "Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback."
back-to-top-navigation: true
toc: true
toc-expand: 2
toc-depth: 4
---

## Overview

Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human
feedback. Various methods include, but not limited to:

- [Direct Preference Optimization (DPO)](#dpo)
- [Identity Preference Optimization (IPO)](#ipo)
- [Kahneman-Tversky Optimization (KTO)](#kto)
- [Odds Ratio Preference Optimization (ORPO)](#orpo)
- [Group Relative Policy Optimization (GRPO)](#grpo)
- [Group Reward-Decoupled Policy Optimization (GDPO)](#gdpo)


## RLHF using Axolotl

::: {.callout-important}
This is a BETA feature and many features are not fully implemented. You are encouraged to open new PRs to improve the integration and functionality.
:::

We rely on the [TRL](https://github.com/huggingface/trl) library for implementations of various RL training methods, which we wrap around to expose in axolotl. Each method has their own supported ways of loading datasets and prompt formats.

::: {.callout-tip}
You can find what each method supports by going into `src/axolotl/prompt_strategies/{method}` where `{method}` is one of our supported methods. The `type: ` can be retrieved from `{method}.{function_name}`.
:::

### DPO

Example config:

```yaml
rl: dpo
datasets:
  - path: Intel/orca_dpo_pairs
    split: train
    type: chatml.intel
  - path: argilla/ultrafeedback-binarized-preferences
    split: train
    type: chatml
```

DPO supports the following types with the following dataset format:

#### chatml.argilla

```json
{
    "system": "...", // optional
    "instruction": "...",
    "chosen_response": "...",
    "rejected_response": "..."
}
```

#### chatml.argilla_chat

```json
{
    "chosen": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ],
    "rejected": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ]
}
```

#### chatml.icr

```json
{
    "system": "...", // optional
    "input": "...",
    "chosen": "...",
    "rejected": "..."
}
```

#### chatml.intel

```json
{
    "system": "...", // optional
    "question": "...",
    "chosen": "...",
    "rejected": "..."
}
```

#### chatml.prompt_pairs

```json
{
    "system": "...", // optional
    "prompt": "...",
    "chosen": "...",
    "rejected": "..."
}
```

#### chatml.ultra

```json
{
    "system": "...", // optional
    "prompt": "...",
    "chosen": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ],
    "rejected": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ]
}
```

#### llama3.argilla

```json
{
    "system": "...", // optional
    "instruction": "...",
    "chosen_response": "...",
    "rejected_response": "..."
}
```

#### llama3.argilla_chat

```json
{
    "chosen": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ],
    "rejected": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ]
}
```

#### llama3.icr

```json
{
    "system": "...", // optional
    "input": "...",
    "chosen": "...",
    "rejected": "..."
}
```

#### llama3.intel

```json
{
    "system": "...", // optional
    "question": "...",
    "chosen": "...",
    "rejected": "..."
}
```

#### llama3.prompt_pairs

```json
{
    "system": "...", // optional
    "prompt": "...",
    "chosen": "...",
    "rejected": "..."
}
```

#### llama3.ultra

```json
{
    "system": "...", // optional
    "prompt": "...",
    "chosen": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ],
    "rejected": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ]
}
```

#### zephyr.nectar

```json
{
    "prompt": "...",
    "answers": [
        {
            "answer": "...",
            "rank": 1
        },
        {
            "answer": "...",
            "rank": 2
        }
        // ... more answers with ranks
    ]
}
```

#### chat_template.argilla_chat

```json
{
    "chosen": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ],
    "rejected": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ]
}
```

#### chat_template.default

```yaml
rl: dpo
datasets:
  - path: ...
    split: train
    type: chat_template.default
    field_messages: "messages"
    field_chosen: "chosen"
    field_rejected: "rejected"
    message_property_mappings:
      role: role
      content: content
    roles:
      user: ["user"]
      assistant: ["assistant"]
      system: ["system"]
```

Sample input format:

```json
{
    "messages": [
        {
            "role": "system",
            "content": "..."
        },
        {
            "role": "user",
            "content": "..."
        },
        // ... more messages
    ],
    "chosen": {
        "role": "assistant",
        "content": "..."
    },
    "rejected": {
        "role": "assistant",
        "content": "..."
    }
}
```

#### user_defined.default

For custom behaviors,

```yaml
rl: dpo
datasets:
  - path: ...
    split: train
    type:
      field_prompt: "prompt"
      field_system: "system"
      field_chosen: "chosen"
      field_rejected: "rejected"
      prompt_format: "{prompt}"
      chosen_format: "{chosen}"
      rejected_format: "{rejected}"
```

The input format is a simple JSON input with customizable fields based on the above config.

```json
{
    "system": "...",  // optional
    "prompt": "...",
    "chosen": "...",
    "rejected": "..."
}
```

### IPO

As IPO is just DPO with a different loss function, all supported dataset formats for [DPO](#dpo) are also supported for IPO.

```yaml
rl: ipo
```

### ORPO

Paper: https://arxiv.org/abs/2403.07691

```yaml
rl: orpo
orpo_alpha: 0.1
remove_unused_columns: false

chat_template: chatml
datasets:
  - path: argilla/ultrafeedback-binarized-preferences-cleaned
    type: chat_template.argilla
```

ORPO supports the following types with the following dataset format:

#### chat_template.argilla

```json
{
    "system": "...",  // optional
    "prompt": "...",  // if available, will be taken as user message for single-turn instead of from list below

    // chosen/rejected should be same till last content and only even-number of alternating user/assistant turns
    "chosen": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ],
    "rejected": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ]
}
```

### KTO

```yaml
rl: kto
rl_beta: 0.1  # default
kto_desirable_weight: 1.0  # default
kto_undesirable_weight: 1.0  # default

remove_unused_columns: false

datasets:
  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
    type: llama3.ultra
    split: train

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
```

KTO supports the following types with the following dataset format:

#### chatml.argilla

```json
{
    "system": "...", // optional
    "instruction": "...",
    "completion": "..."
}
```

#### chatml.argilla_chat

```json
{
    "chosen": [
        {"role": "user", "content": "..."}
    ],
    "completion": [
        {"role": "assistant", "content": "..."}
    ]
}
```

#### chatml.intel

```json
{
    "system": "...", // optional
    "question": "...",
    "completion": "..."
}
```

#### chatml.prompt_pairs

```json
{
    "system": "...", // optional
    "prompt": "...",
    "completion": "..."
}
```

#### chatml.ultra

```json
{
    "system": "...", // optional
    "prompt": "...",
    "completion": "..."
}
```

#### llama3.argilla

```json
{
    "system": "...", // optional
    "instruction": "...",
    "completion": "..."
}
```

#### llama3.argilla_chat

```json
{
    "completion": [
        {"role": "user", "content": "..."},
        {"role": "assistant", "content": "..."}
    ]
}
```

#### llama3.intel

```json
{
    "system": "...", // optional
    "question": "...",
    "completion": "..."
}
```

#### llama3.prompt_pairs

```json
{
    "system": "...", // optional
    "prompt": "...",
    "completion": "..."
}
```

#### llama3.ultra

```json
{
    "system": "...", // optional
    "prompt": "...",
    "completion": "..."
}
```

#### user_defined.default

For custom behaviors,

```yaml
rl: kto
datasets:
  - path: ...
    split: train
    type:
      field_prompt: "prompt"
      field_system: "system"
      field_completion: "completion"
      field_label: "label"
      prompt_format: "{prompt}"
      completion_format: "{completion}"
```

The input format is a simple JSON input with customizable fields based on the above config.

```json
{
    "system": "...",  // optional
    "prompt": "...",
    "completion": "...",
    "label": "..."
}
```

### GRPO

::: {.callout-tip}
Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/grpo_code).
:::

In the latest GRPO implementation, `vLLM` is used to significantly speedup trajectory generation during training. In this example, we're using 4 GPUs - 2 for training, and 2 for vLLM:

::: {.callout-important}
Make sure you've installed the correct version of vLLM by including it as an extra when installing axolotl, e.g. `pip install axolotl[vllm]`.
:::

```yaml
base_model: Qwen/Qwen2.5-1.5B-Instruct

vllm:
    host: 0.0.0.0
    port: 8000
    tensor_parallel_size: 2
    gpu_memory_utilization: 0.85
    dtype: auto
    # max_model_len: # you may find it useful to set the vLLM model context length if you know this beforehand

rl: grpo
trl:
    use_vllm: true
    vllm_server_host: 0.0.0.0
    vllm_server_port: 8000
    vllm_server_timeout: 300
```

```bash
CUDA_VISIBLE_DEVICES=2,3 axolotl vllm-serve grpo.yaml
```

Your `vLLM` instance will now attempt to spin up, and it's time to kick off training utilizing our remaining two GPUs. In another terminal, execute:

```bash
CUDA_VISIBLE_DEVICES=0,1 axolotl train grpo.yaml --num-processes 2
```

::: {.callout-note}
Due to TRL's implementation with vLLM, the vLLM instance must use the last N GPUs instead of the first N GPUs. This is why in the example above, we use `CUDA_VISIBLE_DEVICES=2,3` for the vLLM instance.
:::

#### Reward functions

GRPO uses custom reward functions and transformations. Please have them ready locally.

For example, to load OpenAI's GSM8K and use a random reward for completions:

```python
# rewards.py
import random

def rand_reward_func(completions, **kwargs) -> list[float]:
    return [random.uniform(0, 1) for _ in completions]

def oai_gsm8k_transform(cfg, *args, **kwargs):
    def transform_fn(example, tokenizer=None):
        label = example["answer"].split("####")[-1].strip().replace(",", "")
        return {
            "prompt": [{"role": "user", "content": example["question"]},],
            "answer": label,
        }
    return transform_fn, {"remove_columns": ["question"]}
```

```yaml
rl: grpo

trl:
    beta: 0.001
    max_completion_length: 256
    use_vllm: True
    num_generations: 4
    reward_funcs: ["rewards.rand_reward_func"]    # format: '{file_name}.{fn_name}'
    reward_weights: [1.0]
datasets:
  - path: openai/gsm8k
    name: main
    type: rewards.oai_gsm8k_transform  # format: '{file_name}.{fn_name}'
```

To see other examples of custom reward functions, please see [TRL GRPO Docs](https://github.com/huggingface/trl/blob/main/docs/source/grpo_trainer.md#using-a-custom-reward-function).

To see all configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/v0.9.2/src/axolotl/utils/schemas/trl.py).

#### OpenEnv Rollout Functions

GRPO supports custom rollout functions for OpenEnv-style environments, enabling interactive tasks like web browsing, code execution, or tool use. This allows you to implement custom generation logic that interacts with external environments.

For example, to implement a simple math-solving environment with step-by-step verification:

```python
# math_env.py
import re

def math_solver_rollout(model, processing_class, prompts, generation_config=None):
    """
    Custom rollout function that generates step-by-step math solutions.

    Args:
        model: The language model
        processing_class: The tokenizer/processing_class
        prompts: List of prompt dicts (with 'messages' key for chat format)
        generation_config: Optional generation configuration

    Returns:
        List of completion strings
    """
    completions = []

    for prompt in prompts:
        # Apply chat template to prompt
        messages = prompt.get("messages", [])
        formatted_prompt = processing_class.apply_chat_template(
            messages, processing_class=False, add_generation_prompt=True
        )

        # Generate step-by-step solution
        full_response = ""
        for step in range(5):  # Max 5 reasoning steps
            current_input = formatted_prompt + full_response + "\nNext step:"
            inputs = processing_class(current_input, return_tensors="pt").to(model.device)

            outputs = model.generate(
                **inputs,
                max_new_tokens=100,
                generation_config=generation_config,
            )
            step_text = processing_class.decode(
                outputs[0][inputs.input_ids.shape[1]:],
                skip_special_tokens=True
            )

            # Check if solution is complete
            if "FINAL ANSWER:" in step_text:
                full_response += step_text
                break
            full_response += step_text + "\n"

        completions.append(full_response)

    return completions

def math_reward(prompts, completions, answers, **kwargs):
    """Reward function that checks mathematical correctness"""
    rewards = []
    for completion, correct_answer in zip(completions, answers):
        # Extract predicted answer
        match = re.search(r"FINAL ANSWER:\s*(.+)", completion)
        predicted = match.group(1).strip() if match else ""

        # Compare with correct answer
        reward = 1.0 if predicted == str(correct_answer) else 0.0
        rewards.append(reward)

    return rewards

def math_transform(cfg, *args, **kwargs):
    """Transform dataset to GRPO format with answer field"""
    def transform_fn(example, processing_class=None):
        return {
            "prompt": [{"role": "user", "content": example["question"]}],
            "answer": str(example["answer"]),
        }
    return transform_fn, {"remove_columns": ["question"]}
```

```yaml
rl: grpo

trl:
  beta: 0.001
  max_completion_length: 512
  num_generations: 4
  rollout_func: "math_env.math_solver_rollout"  # Custom rollout function
  reward_funcs: ["math_env.math_reward"]
  reward_weights: [1.0]

datasets:
  - path: openai/gsm8k
    name: main
    type: math_env.math_transform
```

The `rollout_func` parameter accepts a fully qualified name (e.g., `module_name.function_name`) that points to a callable function in your local directory. The function receives:

- `model`: The language model
- `processing_class`: The tokenizer/processing class
- `prompts`: List of prompt dictionaries
- `generation_config` (optional): Generation configuration

And should return a list of completion strings.

For more OpenEnv examples, see [TRL OpenEnv Documentation](https://huggingface.co/docs/trl/main/en/openenv).

#### GRPO with DAPO/Dr. GRPO loss

The DAPO paper and subsequently Dr. GRPO paper proposed an alternative loss function for GRPO to remediate the penalty in longer responses.

```yaml
trl:
  loss_type: dr_grpo
  # Normalizes loss based on max completion length (default: 256)
  max_completion_length:
```

For more information, see [GRPO docs](https://huggingface.co/docs/trl/v0.17.0/en/grpo_trainer#loss-types).

#### Async GRPO

Async GRPO overlaps vLLM generation with training by producing rollouts in a background thread. While the model trains on the current batch, the next batch is already being generated. This can significantly reduce wall-clock time per step.

```yaml
trl:
  use_data_producer: true     # Enable data producer protocol
  use_vllm: true
  async_prefetch: true         # Generate rollouts in background thread
  prefetch_depth: 1            # Number of rollouts to prefetch
  vllm_sync_interval: 2        # Sync weights to vLLM every N steps
```

::: {.callout-note}
Because the background thread generates completions with slightly stale model weights, async GRPO uses importance sampling correction to account for the distribution shift. This is controlled by `vllm_importance_sampling_correction: true` (default when async is enabled).
:::

##### vLLM LoRA Sync

By default, weight sync to vLLM merges the LoRA adapter into the base model and broadcasts all parameters via NCCL. LoRA sync is a faster alternative that saves only the adapter weights to the filesystem and has vLLM load them natively using Punica kernels.

```yaml
adapter: lora
lora_r: 32
lora_alpha: 64
lora_target_linear: true

trl:
  vllm_lora_sync: true         # Enable native LoRA sync
```

When `vllm_lora_sync: true` is set, axolotl automatically selects the LoRA-aware vLLM serve module. Start vLLM as usual:

```bash
CUDA_VISIBLE_DEVICES=0 axolotl vllm-serve config.yaml
```

Then start training on a separate GPU:

```bash
CUDA_VISIBLE_DEVICES=1 axolotl train config.yaml
```

::: {.callout-tip}
LoRA sync is especially beneficial with multi-GPU training (FSDP/DeepSpeed), where NCCL merge-sync can cause GPU contention with vLLM generation.
:::

##### Streaming Partial Batch

Instead of scoring the entire batch at once, streaming mode scores one prompt group at a time. This enables finer-grained zero-advantage skipping and reduces peak memory usage during scoring.

```yaml
trl:
  streaming_partial_batch: true
```

##### Importance Sampling Correction

When using async prefetch, completions are generated from a slightly older version of the model. Importance sampling (IS) correction adjusts the policy gradient to account for this distribution shift.

```yaml
trl:
  vllm_importance_sampling_correction: true   # Enable IS correction
  importance_sampling_level: token             # 'token' or 'sequence'
  off_policy_mask_threshold: 0.5              # Mask sequences with IS ratio below this
```

- `importance_sampling_level: token` applies per-token IS ratios (recommended with Liger kernel)
- `importance_sampling_level: sequence` applies per-sequence IS ratios
- `off_policy_mask_threshold` masks out sequences where the IS ratio indicates they are too far off-policy

##### Replay Buffer

The replay buffer caches rollout groups that had learning signal (non-zero reward variance) and uses them to replace zero-signal groups in later batches.

```yaml
trl:
  replay_buffer_size: 100       # Max cached groups (0 = disabled)
  replay_recompute_logps: true  # Recompute log-probs for replayed data (recommended)
```

::: {.callout-note}
When `replay_recompute_logps: true` (default), old log-probabilities are recomputed using the current model weights. This fixes the IS mismatch that would otherwise occur when replaying stale data.
:::

##### Deferred Re-rolling

Failed prompts (where the model produces zero reward for all generations) are buffered and re-injected into later batches when the model may be better equipped to solve them.

```yaml
trl:
  reroll_start_fraction: 0.5    # Start re-rolling after 50% of training
  reroll_max_groups: 1          # Max groups to replace per batch
```

##### Zero-Advantage Batch Skipping

When all advantages in a micro-batch are zero (no learning signal), the forward/backward pass is skipped entirely. This is enabled by default and logged as `skipped_zero_adv_batches=1`.

```yaml
trl:
  skip_zero_advantage_batches: true   # default
```

##### Parallel Reward Workers

Reward functions that use `signal.alarm()` (e.g., `math_verify`) must run in the main thread. Parallel reward workers use subprocesses to work around this limitation while enabling concurrent reward computation.

```yaml
trl:
  reward_num_workers: 4         # Number of subprocess workers (1 = no parallelism)
```

##### Full Async GRPO Example

```yaml
base_model: Qwen/Qwen2.5-1.5B-Instruct

vllm:
    host: 0.0.0.0
    port: 8000
    gpu_memory_utilization: 0.35
    dtype: auto

adapter: lora
lora_r: 32
lora_alpha: 64
lora_target_linear: true

rl: grpo
trl:
  use_data_producer: true
  use_vllm: true
  async_prefetch: true
  prefetch_depth: 1
  vllm_sync_interval: 2
  vllm_lora_sync: true
  streaming_partial_batch: true
  vllm_importance_sampling_correction: true
  off_policy_mask_threshold: 0.5
  importance_sampling_level: token
  num_generations: 8
  max_completion_length: 512
  reward_funcs:
    - rewards.accuracy_reward
  reroll_start_fraction: 0.5
  replay_buffer_size: 100
  reward_num_workers: 4
  skip_zero_advantage_batches: true

datasets:
  - path: AI-MO/NuminaMath-TIR
    type: rewards.prompt_transform
    split: train

gradient_accumulation_steps: 4
micro_batch_size: 2
max_steps: 500
learning_rate: 1e-5
bf16: true
gradient_checkpointing: true
```

```bash
# Terminal 1: Start vLLM on GPU 0
CUDA_VISIBLE_DEVICES=0 axolotl vllm-serve config.yaml

# Terminal 2: Train on GPU 1
CUDA_VISIBLE_DEVICES=1 axolotl train config.yaml
```

##### Multi-GPU Async GRPO

Async GRPO supports FSDP and DeepSpeed ZeRO-3 for multi-GPU training. vLLM runs on one GPU while training is distributed across the remaining GPUs.

**FSDP:**

```yaml
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
gradient_checkpointing_kwargs:
  use_reentrant: false
```

**DeepSpeed ZeRO-3:**

```yaml
deepspeed: deepspeed_configs/zero3_bf16.json
gradient_checkpointing_kwargs:
  use_reentrant: true   # Required for ZeRO-3
```

```bash
# Terminal 1: Start vLLM on GPU 0
CUDA_VISIBLE_DEVICES=0 axolotl vllm-serve config.yaml

# Terminal 2: Train on GPUs 0,1
CUDA_VISIBLE_DEVICES=0,1 accelerate launch --num_processes 2 -m axolotl.cli.train config.yaml
```

::: {.callout-important}
With multi-GPU async prefetch, only rank 0 generates completions in the background thread. Results are broadcast to all ranks on the main thread. This avoids FSDP/DeepSpeed collective deadlocks from unsynchronized background threads.
:::

### GDPO

GDPO (Group Reward-Decoupled Policy Optimization) extends GRPO for multi-reward training. It addresses the **reward advantage collapse** problem by normalizing each reward function independently before combining them.

::: {.callout-tip}
Use GDPO when training with multiple reward functions. For single reward, GRPO and GDPO produce equivalent results.
:::

Paper: [https://arxiv.org/pdf/2501.05242](https://arxiv.org/pdf/2501.05242)

GDPO uses TRL's native `multi_objective_aggregation` parameter under the hood. When you set `rl: gdpo`, axolotl automatically configures TRL to use `normalize_then_sum` aggregation.

```yaml
base_model: Qwen/Qwen2.5-1.5B-Instruct

vllm:
    host: 0.0.0.0
    port: 8000
    tensor_parallel_size: 2
    gpu_memory_utilization: 0.85

rl: gdpo

trl:
    beta: 0.001
    max_completion_length: 256
    use_vllm: true
    num_generations: 4
    reward_funcs:
        - rewards.format_reward
        - rewards.correctness_reward
    reward_weights: [1.0, 2.0]

datasets:
    - path: openai/gsm8k
      name: main
      type: rewards.oai_gsm8k_transform
```

You can also use GRPO with explicit aggregation control:

```yaml
rl: grpo
trl:
    multi_objective_aggregation: normalize_then_sum  # GDPO behavior
    # or: sum_then_normalize  # Default GRPO behavior
```

#### GDPO vs GRPO

| Aspect | GRPO | GDPO |
|--------|------|------|
| **Aggregation** | `sum_then_normalize` | `normalize_then_sum` |
| **Multi-reward** | May collapse advantages | Preserves reward signals |
| **Single reward** | Standard behavior | Equivalent to GRPO |

#### Why GDPO?

When using multiple rewards with GRPO, different reward combinations can produce identical advantages:

```
# Example: format + correctness rewards
[format=0, correct=3] → sum=3
[format=1, correct=2] → sum=3  ← GRPO sees these as equal!
[format=2, correct=1] → sum=3
[format=3, correct=0] → sum=3
```

GDPO normalizes each reward independently, preserving their relative differences.

#### Reward Functions

GDPO uses the same reward function format as GRPO:

```python
# rewards.py
def format_reward(completions, **kwargs) -> list[float]:
    return [1.0 if len(c) > 10 else 0.0 for c in completions]

def correctness_reward(completions, answers, **kwargs) -> list[float]:
    rewards = []
    for completion, answer in zip(completions, answers):
        # Your scoring logic here
        rewards.append(score)
    return rewards
```

#### Sequence Parallelism

GDPO supports sequence parallelism for long-context training:

```yaml
rl: gdpo
context_parallel_size: 2
```

### SimPO

SimPO uses [CPOTrainer](https://huggingface.co/docs/trl/main/en/cpo_trainer) but with alternative loss function.

```yaml
rl: simpo
rl_beta: 0.1  # default in CPOTrainer
cpo_alpha: 1.0  # default in CPOTrainer
simpo_gamma: 0.5  # default in CPOTrainer
```

This method uses the same dataset format as [DPO](#dpo).

### Using local dataset files

```yaml
datasets:
  - ds_type: json
    data_files:
      - orca_rlhf.jsonl
    split: train
    type: chatml.intel
```

### TRL auto-unwrapping for PEFT

TRL supports auto-unwrapping PEFT models for RL training paradigms which rely on a reference model. This significantly reduces memory pressure as an additional refreference model does not need to be loaded, and reference model log-probabilities can be obtained by disabling PEFT adapters. This is enabled by default. To turn it off, pass the following config:

```yaml
# load ref model when adapter training.
rl_adapter_ref_model: true
```
