# TRL - Transformers Reinforcement Learning
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<p>A comprehensive library to post-train foundation models</p>
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## 🎉 What's New
**🚀 Training on AMD GPUs:** TRL's trainers now run on [AMD Instinct GPUs](https://huggingface.co/docs/trl/amd_gpus) through PyTorch's ROCm build.
**🔥 Beam:** Fine-tune [Beam](https://huggingface.co/reflection/Beam-1.0-501B-A23B) with [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer) on a single node of 8 B300s. See the [LoRA example](https://github.com/huggingface/trl/tree/main/examples/sft_beam).
**🔥 Muse Spark Open:** Fine-tune [Muse Spark Open](https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B) with [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer), sharded across your GPUs at load time. See the [8-node LoRA example](https://github.com/huggingface/trl/tree/main/examples/sft_muse_spark_open).
**🚀 Training on AWS Trainium:** TRL's trainers now run on [AWS Trainium](https://huggingface.co/docs/trl/aws_trainium) through TorchNeuron, PyTorch's native Neuron backend.
## Overview
TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), and Direct Preference Optimization (DPO). Built on top of the [🤗 Transformers](https://github.com/huggingface/transformers) ecosystem, TRL supports a variety of model architectures and modalities, and can be scaled-up across various hardware setups.
## Highlights
- **Trainers**: Various fine-tuning methods are easily accessible via trainers like [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer), [`GRPOTrainer`](https://huggingface.co/docs/trl/grpo_trainer), [`DPOTrainer`](https://huggingface.co/docs/trl/dpo_trainer), [`KTOTrainer`](https://huggingface.co/docs/trl/kto_trainer) and more.
- **Efficient and scalable**:
- Leverages [🤗 Accelerate](https://github.com/huggingface/accelerate) to scale from single GPU to multi-node clusters using methods like [DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) and [DeepSpeed](https://github.com/deepspeedai/DeepSpeed).
- Full integration with [🤗 PEFT](https://github.com/huggingface/peft) enables training on large models with modest hardware via quantization and LoRA/QLoRA.
- Integrates [🦥 Unsloth](https://github.com/unslothai/unsloth) for accelerating training using optimized kernels.
- **Command Line Interface (CLI)**: A simple interface lets you fine-tune with models without needing to write code.
## Installation
### Python Package
Install the library using `pip`:
```bash
pip install trl
```
### From source
If you want to use the latest features before an official release, you can install TRL from source:
```bash
pip install git+https://github.com/huggingface/trl.git
```
### Repository
If you want to use the examples you can clone the repository with the following command:
```bash
git clone https://github.com/huggingface/trl.git
```
## Quick Start
For more flexibility and control over training, TRL provides dedicated trainer classes to post-train language models or PEFT adapters on a custom dataset. Each trainer in TRL is a light wrapper around the 🤗 Transformers trainer and natively supports distributed training methods like DDP, DeepSpeed ZeRO, and FSDP.
### `SFTTrainer`
Here is a basic example of how to use the [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer):
```python
from trl import SFTTrainer
from datasets import load_dataset
dataset = load_dataset("trl-lib/Capybara", split="train")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=dataset,
)
trainer.train()
```
### `GRPOTrainer`
[`GRPOTrainer`](https://huggingface.co/docs/trl/grpo_trainer) implements the [Group Relative Policy Optimization (GRPO) algorithm](https://huggingface.co/papers/2402.03300) that is more memory-efficient than PPO and was used to train [Deepseek AI's R1](https://huggingface.co/deepseek-ai/DeepSeek-R1). `accuracy_reward` needs math-verify, which you can install with `pip install trl[math_verify]`.
```python
from datasets import load_dataset
from trl import GRPOTrainer
from trl.rewards import accuracy_reward
dataset = load_dataset("trl-lib/DeepMath-103K", split="train")
trainer = GRPOTrainer(
model="Qwen/Qwen2.5-0.5B-Instruct",
reward_funcs=accuracy_reward,
train_dataset=dataset,
)
trainer.train()
```
> [!NOTE]
> For reasoning models, use the `reasoning_accuracy_reward()` function for better results.
### `DPOTrainer`
[`DPOTrainer`](https://huggingface.co/docs/trl/dpo_trainer) implements the popular [Direct Preference Optimization (DPO) algorithm](https://huggingface.co/papers/2305.18290) that was used to post-train [Llama 3](https://huggingface.co/papers/2407.21783) and many other models. Here is a basic example of how to use the `DPOTrainer`:
```python
from datasets import load_dataset
from trl import DPOTrainer
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
trainer = DPOTrainer(
model="Qwen/Qwen3-0.6B",
train_dataset=dataset,
)
trainer.train()
```
### `KTOTrainer`
[`KTOTrainer`](https://huggingface.co/docs/trl/kto_trainer) implements the [Kahneman-Tversky Optimization (KTO) algorithm](https://huggingface.co/papers/2402.01306), which aligns models from simple binary (desirable / undesirable) feedback rather than paired preferences. Here is a basic example of how to use the `KTOTrainer`:
```python
from datasets import load_dataset
from trl import KTOTrainer
dataset = load_dataset("trl-lib/kto-mix-14k", split="train")
trainer = KTOTrainer(
model="Qwen/Qwen3-0.6B",
train_dataset=dataset,
)
trainer.train()
```
### `RewardTrainer`
Here is a basic example of how to use the [`RewardTrainer`](https://huggingface.co/docs/trl/reward_trainer):
```python
from trl import RewardTrainer
from datasets import load_dataset
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
trainer = RewardTrainer(
model="Qwen/Qwen2.5-0.5B-Instruct",
train_dataset=dataset,
)
trainer.train()
```
## Command Line Interface (CLI)
You can use the TRL Command Line Interface (CLI) to quickly get started with post-training methods like Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO):
**SFT:**
```bash
trl sft --model_name_or_path Qwen/Qwen2.5-0.5B \
--dataset_name trl-lib/Capybara \
--output_dir Qwen2.5-0.5B-SFT
```
**DPO:**
```bash
trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
--dataset_name argilla/Capybara-Preferences \
--output_dir Qwen2.5-0.5B-DPO
```
**KTO:**
```bash
trl kto --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
--dataset_name trl-lib/kto-mix-14k \
--output_dir Qwen2.5-0.5B-KTO
```
Read more about CLI in the [relevant documentation section](https://huggingface.co/docs/trl/clis) or use `--help` for more details.
## Development
If you want to contribute to `trl` or customize it to your needs make sure to read the [contribution guide](https://github.com/huggingface/trl/blob/main/CONTRIBUTING.md) and make sure you make a dev install:
```bash
git clone https://github.com/huggingface/trl.git
cd trl/
pip install -e .[dev]
```
## Experimental
A minimal incubation area is available under `trl.experimental` for unstable / fast-evolving features. Anything there may change or be removed in any release without notice.
Example:
```python
from trl.experimental.new_trainer import NewTrainer
```
Read more in the [Experimental docs](https://huggingface.co/docs/trl/experimental_overview).
## Citation
```bibtex
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
```
## License
This repository's source code is available under the [Apache-2.0 License](LICENSE).