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fine-tuning-with-trl

Fine-tune LLMs with TRL reinforcement learning and HuggingFace Transformers.

Updated Apr 30, 2026
Search Tags:#fine-tuning#LLM#reinforcement learning#TRL#preference alignment
One-click install
npx skills add https://github.com/Ced3-han/Harness-Settings --skill fine-tuning-with-trl-ced3-han
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Ced3-han/Harness-Settings/tree/main/skills/trl-fine-tuning
Command: npx skills add https://github.com/Ced3-han/Harness-Settings --skill fine-tuning-with-trl-ced3-han

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl, transformers, datasets, peft, accelerate, torch, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps in fine-tuning Large Language Models (LLMs) using Transformer Reinforcement Learning (TRL) techniques, addressing the need for reinforcement learning, preference alignment, and human feedback.

Core Features & Use Cases

  • Reinforcement Learning: Offers methods like SFT, DPO, PPO, and GRPO for RLHF, preference alignment, and reward model training.
  • Fine-Tuning: Supports SFT for instruction tuning and DPO for preference alignment, with options for PPO and GRPO for reward optimization.
  • HuggingFace Compatibility: Integrates with HuggingFace Transformers for ease of use.
  • Use Case: Ideal for researchers and developers who need to align LLMs with human preferences or train models from human feedback.

Quick Start

Install the Skill and use the trl command-line interface to fine-tune your LLM with TRL techniques.

Frequently Asked Questions about fine-tuning-with-trl

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

FAQPage Schema
How do I fine-tune LLMs with TRL for reinforcement learning and preference alignment?▼

Fine-tune LLMs with TRL by using methods like SFT, DPO, PPO, and GRPO for reinforcement learning, preference alignment, and reward model training. It integrates with HuggingFace Transformers for ease of use.

What is the best way to align LLMs with human preferences using HuggingFace Transformers?▼

Align LLMs with human preferences using DPO and PPO techniques within the TRL framework. This approach integrates directly with HuggingFace Transformers to optimize models based on human feedback.

Can I use PEFT and accelerate for LLM fine-tuning with TRL?▼

Yes, LLM fine-tuning with TRL supports PEFT and accelerate. These libraries are required dependencies alongside trl, transformers, datasets, and torch to execute reinforcement learning and preference alignment workflows.

When do I need DPO versus PPO for reward model training in TRL?▼

Use DPO for preference alignment and PPO for reward optimization during TRL fine-tuning. SFT is also available for instruction tuning, while GRPO offers another method for reinforcement learning from human feedback.

Does TRL support instruction tuning and reward model training for Large Language Models?▼

Yes, TRL supports instruction tuning through SFT and reward model training through PPO and GRPO. It provides comprehensive tools for reinforcement learning and preference alignment using HuggingFace Transformers.

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