TRL - Transformers Reinforcement Learning
A comprehensive library to post-train foundation models
🎉 What's New
📜 Training beyond 1M tokens: A new long context guide walks through the four things that break as sequences grow — the loss, the positions, the activations and the memory of a single GPU — and ends on an example that trains Qwen3-8B on million-token sequences on one 8-GPU node.
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,KTOTrainerand 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()
[!NOTE] For reasoning models, use the
reasoning_accuracy_reward()function for better results.
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.
5 maintainers
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file trl-1.13.0.tar.gz.
File metadata
- Download URL: trl-1.13.0.tar.gz
- Upload date:
- Size: 825.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.12.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0905a7813e246c8449fc8021492134cdf80402ec9ad9ae7f8a72876f77eea9d7
|
|
| MD5 |
13e5bcd3865218c3eb2888c1f022e2ff
|
|
| BLAKE2b-256 |
3dec2ea0856273ec5a5414555c81a2b9a9cdc9dc91e518ce2da656912ccb01f2
|
File details
Details for the file trl-1.13.0-py3-none-any.whl.
File metadata
- Download URL: trl-1.13.0-py3-none-any.whl
- Upload date:
- Size: 1.0 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.12.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7c7ae0c72e969450221cf4c7cfe69c532c5a83c727204f1c8ced38a9b79134ec
|
|
| MD5 |
7ba8022dc657b19dc429a3f18e0a5984
|
|
| BLAKE2b-256 |
d46694f4f8f2967a5ec02fdfa61c08423ffeba652d9407fb6e4f5e8eaf1c8e34
|
