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fine-tuning-with-trl

Fine-tune LLMs with SFT, DPO, PPO, and GRPO using the TRL library.

4|1|Updated Jan 7, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#trl#rlhf#preference alignment
One-click install
npx skills add https://github.com/an8079/take-skills --skill fine-tuning-with-trl-an8079
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/an8079/take-skills/tree/main/skills/fine-tuning-with-trl
Command: npx skills add https://github.com/an8079/take-skills --skill fine-tuning-with-trl-an8079

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of aligning Large Language Models (LLMs) with human preferences and instructions, making them more helpful, harmless, and honest.

Core Features & Use Cases

  • Reinforcement Learning from Human Feedback (RLHF): Implements full RLHF pipelines including Supervised Fine-Tuning (SFT), Reward Model training, and Proximal Policy Optimization (PPO).
  • Direct Preference Optimization (DPO): Offers a simpler method for preference alignment without needing a separate reward model.
  • Memory-Efficient RL: Provides Group Relative Policy Optimization (GRPO) for training with limited memory.
  • Use Case: You have a base LLM and a dataset of user preferences (which responses are better than others). Use this Skill to fine-tune the LLM to generate responses that better match human expectations and instructions.

Quick Start

Use the fine-tuning-with-trl skill to perform Direct Preference Optimization on the 'Qwen/Qwen2.5-0.5B-Instruct' model using the 'argilla/Capybara-Preferences' dataset.

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 an LLM with human preferences using reinforcement learning?▼

You can fine-tune LLMs with human preferences using reinforcement learning by implementing full pipelines like Supervised Fine-Tuning, Reward Model training, and Proximal Policy Optimization through the TRL library.

What is the difference between DPO and PPO for preference alignment?▼

DPO offers a simpler method for preference alignment without needing a separate reward model, whereas PPO requires training a dedicated Reward Model before applying Proximal Policy Optimization to the LLM.

Can I use TRL to train large language models with limited GPU memory?▼

Yes, you can train LLMs with limited memory using TRL by applying Group Relative Policy Optimization (GRPO), which provides a memory-efficient approach to reinforcement learning and preference alignment.

How do I align a base LLM with a dataset of user preferences?▼

To align a base LLM with user preferences, you apply fine-tuning techniques like DPO or PPO using a preference dataset to generate responses that better match human expectations and instructions.

Does TRL integrate with HuggingFace Transformers and Datasets?▼

Yes, TRL integrates seamlessly with HuggingFace Transformers and Datasets, allowing you to load models and preference data directly within your reinforcement learning and fine-tuning workflow.

What are the prerequisites for running RLHF with TRL?▼

To run RLHF with TRL, you need dependencies including transformers, datasets, peft, accelerate, and torch, along with a base LLM and a dataset of user preferences for training.

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