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TRL

github.comlibraryself-hosted

TRL is a self-hosted library in the Fine-tuning & Training layer of the AI stack. Hugging Face library of post-training trainers for SFT, DPO and GRPO.

By Lattice editorial · Facts verified · method

licence
Apache-2.0
language
Python
cost
free

Use TRL when

Post-training trainers for SFT, DPO and GRPO on top of any PEFT setup.

Skip TRL when

You are training from scratch rather than aligning an existing model.

Produces the weights that layer 1 serves. — that is what this layer of the stack is answerable for. See the full Fine-tuning & Training section for the rest of the options.

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Lattice badge: TRL verified 2026-09
[![TRL on Lattice: verified 2026-09](https://lattice.kunjshah.in/fine-tuning/trl/badge.svg)](https://lattice.kunjshah.in/fine-tuning/trl)

Quick answers

Is TRL open source?
Yes. TRL is open source under the Apache-2.0 licence. It is free to use.
What are the alternatives to TRL?
The closest alternatives to TRL are PEFT, LLaMA-Factory and torchtune.

Reading on training

Alternatives to TRL

compare 3 →

Listed as an alternative to

  • LLaMA-Factory
  • PEFT
  • torchtune
  • Hugging Face TRL

Also in training

  • Unsloth
  • Axolotl
  • LLaMA-Factory
  • PEFT
  • DeepSpeed
  • Megatron-LM
  • torchtune
  • Hugging Face TRL
  • Colab
  • Replicate
  • Weights & Biases Launch