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trl

huggingface/trl

Train transformer language models with reinforcement learning

GitHub Website

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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19k stars3.0k forksLast push Sep 18, 2026 Python Apache-2.0

Decision brief

TRL is a Python library for training transformer language models with reinforcement learning, offering dedicated trainer classes for post-training and PEFT adapters, and supporting distributed training methods.

Good fit when

When you need to post-train language models or PEFT adapters on custom datasets with reinforcement learning.
If you require support for distributed training methods such as DDP, DeepSpeed ZeRO, and FSDP.

Avoid when

If your project does not require reinforcement learning for training transformer models.
When you do not need the specific distributed training methods supported by TRL.

Requirements:: TRL requires Python and is built on top of the Hugging Face Transformers library.

Observed Sep 18, 2026 · Source: enrich:decision_facts

trl alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Very active (0d since push); As of Sep 18, 2026
Provenance: Not a fork · Organization account; As of Sep 18, 2026
Security (OSV): No lockfile; As of Sep 18, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Backing

Company context for Hugging Face. Display-only - separate from trust and ranking.

Company: Hugging Face·GitHub org profile·Jul 11, 2026
Employees: 160·Wikidata (P1128 employees)·Jul 11, 2026
Funding: $235,000,000 (2023-08)·GraphCanon curated seed (public press)·Jul 11, 2026
Commercial model: OSS + managed cloud·GraphCanon curated seed·Jul 11, 2026

Install

pip install trl

PyPI

How it fits your stack(2)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Integrates

transformers

TRL integrates with transformers by leveraging transformers' extensive library of pre-trained models as a foundational base for applying reinforcement learning methods such as SFT, GRPO, and DPO to further refine these models. This relationship allows TRL to utilize the robust model architectures provided by transformers while focusing on advanced training techniques.

integrates

awesome-RLHF

TRL is a specific library for training transformer models with reinforcement learning, while awesome-RLHF is a curated list of resources on RLHF. They are related because TRL provides practical tools in the space covered by the resource list.

Relationship graph

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Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

TRL provides dedicated trainer classes for post-training language models or PEFT adapters on custom datasets, supporting distributed training methods.

Capability facts

CLI: CLI entrypoint
Source: pyproject.toml:[project.scripts] · Sep 18, 2026
Languages: python
Source: github.language+pyproject.toml · Sep 18, 2026

Graph entities

Hugging Facecompany Smolagentsframework

README

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 train...

For agents

This page has a .md twin and JSON over the API.

/tools/huggingface-trl.md GET /api/graphcanon/tools/huggingface-trl GET /api/graphcanon/graph?tool=huggingface-trl

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