TRL - Transformers Reinforcement Learning

🎉 What's New

⚗️ DistillationTrainer is now stable: DistillationTrainer graduates to the stable API — on-policy knowledge distillation that matches a teacher's full next-token distribution with a memory-efficient chunked JSD loss and vLLM-powered generation.

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 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, GRPOTrainer, DPOTrainer, KTOTrainer and more.

• Efficient and scalable:

  • Leverages 🤗 Accelerate to scale from single GPU to multi-node clusters using methods like DDP and DeepSpeed.
  • Full integration with 🤗 PEFT enables training on large models with modest hardware via quantization and LoRA/QLoRA.
  • Integrates 🦥 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:

pip install trl

From source

If you want to use the latest features before an official release, you can install TRL from source:

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:

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:

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 implements the Group Relative Policy Optimization (GRPO) algorithm that is more memory-efficient than PPO and was used to train Deepseek AI's R1.

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()

DPOTrainer

DPOTrainer implements the popular Direct Preference Optimization (DPO) algorithm that was used to post-train Llama 3 and many other models. Here is a basic example of how to use the DPOTrainer:

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 implements the Kahneman-Tversky Optimization (KTO) algorithm, which aligns models from simple binary (desirable / undesirable) feedback rather than paired preferences. Here is a basic example of how to use the KTOTrainer:

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:

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:

trl sft --model_name_or_path Qwen/Qwen2.5-0.5B \
    --dataset_name trl-lib/Capybara \
    --output_dir Qwen2.5-0.5B-SFT

DPO:

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:

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 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 and make sure you make a dev install:

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:

from trl.experimental.new_trainer import NewTrainer

Read more in the Experimental docs.

Citation

@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.