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Decision brief
TRL (Train Reinforcement Learning) by Hugging Face provides specialized trainer classes designed for fine-tuning or PEFT adapter post-training on custom datasets, including support for multiple distributed training modes
Good fit when
- You need to fine-tune transformer language models with reinforcement learning using Python.
- Your project requires flexibility and control over the reinforcement learning training process.
Avoid when
- If your task does not involve transformer language models or if you do not plan to use reinforcement learning for model fine-tuning.
- When strict control over training parameters is less critical and a more streamlined framework suffices.
- Requirements:
- Min 8 GB RAM
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 1w
- Provenance
- Not a fork · Organization account
- As of 1w
- Security (OSV)
- No lockfile
- As of 1mo
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Backing
Company context for Hugging Face. Display-only - separate from trust and ranking.
- Company
- Hugging Face·GitHub org profile·1mo
- Employees
- 160·Wikidata (P1128 employees)·1mo
- Funding
- $235,000,000 (2023-08)·GraphCanon curated seed (public press)·1mo
- Commercial model
- OSS + managed cloud·GraphCanon curated seed·1mo
Install
pip install trlHow it fits your stack(2)
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Evidence and technical details
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Overview
TRL from Hugging Face offers dedicated trainer classes for fine-tuning or PEFT adapter post-training on custom datasets, supporting various distributed training methods.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 6, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 6, 2026
Categories
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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 training methods like DDP, DeepSpeed ZeRO, and FSDP.
License
This repository's source code is available under the Apache-2.0 License.
For agents
This page has a .md twin and JSON over the API.