Open-Source AI · Fine-tuning

Axolotl vs TRL

Axolotl vs TRL compared for 2026 — features, license, ease of use, performance and which one to choose. Config-driven fine-tuning for many models vs Align LLMs (SFT, DPO, PPO).

Updated regularly · curated by OpenSourceAI.tech

Choose Axolotl for teams running reproducible training configs. Choose TRL for RLHF, DPO and alignment training.

Axolotl vs TRL at a glance

SpecAxolotlTRL
CategoryFine-tuningFine-tuning
TypeFine-tuning frameworkRLHF / alignment library
LicenseApache-2.0Apache-2.0
Runs locallyYesYes
Primary languagePythonPython
Ease of useAdvancedAdvanced
Best forteams running reproducible training configsRLHF, DPO and alignment training
GitHub stars12.3k19k

How Axolotl and TRL score

🤝 Too close to call — Axolotl and TRL land within a hair (4.1 vs 4.2 / 5). Pick on fit, not on score.
CriterionAxolotlTRL
Popularity3.03.5
Maintenance5.05.0
Ease of use2.52.5
Privacy5.05.0
License freedom5.05.0

Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.

What each one is

Axolotl

Fine-tuning framework · Apache-2.0

Axolotl is a config-driven fine-tuning framework supporting many model families and training techniques through simple YAML files.

  • Reproducible YAML-based training configs
  • Supports many models and techniques (LoRA, QLoRA)
  • Multi-GPU and cloud friendly
See the Axolotl page →

TRL

RLHF / alignment library · Apache-2.0

TRL is Hugging Face's library for post-training and aligning language models with supervised fine-tuning, DPO and reinforcement learning methods like PPO.

  • SFT, DPO and PPO in one library
  • Integrates with PEFT and Accelerate
  • Maintained by Hugging Face
See the TRL page →

Key differences

Axolotl is fine-tuning framework, while TRL is rLHF / alignment library. In short, Axolotl fits teams running reproducible training configs, and TRL fits RLHF, DPO and alignment training.

Which should you choose?

Choose Axolotl for teams running reproducible training configs. Choose TRL for RLHF, DPO and alignment training.

There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.

Frequently asked questions

Is Axolotl or TRL easier to use?

Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.

Are Axolotl and TRL free?

Axolotl is free and open source (Apache-2.0), and TRL is free and open source (Apache-2.0). Neither charges for the core software.

Can I run Axolotl and TRL locally?

Axolotl: yes · TRL: yes. Both can be used without sending your data to a third-party cloud where their setup allows.

Axolotl vs TRL — which should I pick in 2026?

Choose Axolotl for teams running reproducible training configs. Choose TRL for RLHF, DPO and alignment training.

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