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trl-training

Train and fine-tune transformer language models with TRL techniques.

Updated Jun 15, 2026
Search Tags:#fine-tuning#training#TRL#transformer#language model
One-click install
npx skills add https://github.com/Andrew-Girgis/token-holdem --skill trl-training-andrew-girgis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trl-training
Source: https://github.com/Andrew-Girgis/token-holdem/tree/main/.agents/skills/trl-training
Command: npx skills add https://github.com/Andrew-Girgis/token-holdem --skill trl-training-andrew-girgis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires huggingface, transformers, accelerate, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of training and fine-tuning transformer language models using TRL (Transformers Reinforcement Learning), providing a comprehensive solution for various training techniques.

Core Features & Use Cases

  • Training Techniques: Offers support for SFT, DPO, GRPO, KTO, RLOO, and Reward Model training.
  • CLI Commands: Provides a command-line interface for executing training tasks.
  • Integration: Seamlessly integrates with the Hugging Face ecosystem and Transformers library.
  • Use Case: Ideal for researchers and developers looking to train and fine-tune language models for specific tasks.

Quick Start

Run the 'trl sft' command to start supervised fine-tuning on a language model.

Frequently Asked Questions about trl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a transformer language model using TRL?▼

Fine-tune a transformer language model using TRL by running the 'trl sft' command for supervised fine-tuning. It integrates with Hugging Face Transformers to execute training tasks directly from the command line.

What training techniques does TRL support for language models?▼

TRL supports SFT, DPO, GRPO, KTO, RLOO, and Reward Model training for language models. These techniques allow researchers to apply both supervised and reinforcement learning workflows.

Do I need Hugging Face Transformers and Accelerate to train models with TRL?▼

Yes, Hugging Face Transformers and Accelerate are required dependencies to train models with TRL. The Skill relies on these libraries to seamlessly integrate and execute fine-tuning workflows.

Can I use TRL for supervised fine-tuning on a specific task?▼

Yes, you can use TRL for supervised fine-tuning on specific tasks by executing the 'trl sft' command. It is designed to help researchers and developers train language models efficiently.

What is the best way to train a transformer model with reinforcement learning?▼

Train a transformer model with reinforcement learning using TRL methods like DPO, GRPO, KTO, RLOO, or Reward Model training. This Skill provides a command-line interface to execute these techniques.

Why use TRL over other tools for fine-tuning language models?▼

TRL offers a comprehensive solution for fine-tuning language models by supporting multiple training techniques and seamlessly integrating with the Hugging Face ecosystem. It provides direct CLI commands for easy execution.

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