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fine-tuning-with-trl

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

Updated Mar 12, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#dpo#trl#rlhf#preference alignment
One-click install
npx skills add https://github.com/GarrettRoi/open-manus --skill fine-tuning-with-trl-garrettroi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/GarrettRoi/open-manus/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/GarrettRoi/open-manus --skill fine-tuning-with-trl-garrettroi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the process 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): Aligns models directly using preference data without needing a separate reward model, simplifying the alignment process.
  • Memory-Efficient RL: Utilizes Group Relative Policy Optimization (GRPO) for training with reduced memory footprints.
  • Use Case: You have a base LLM that generates factually correct but sometimes unhelpful or biased responses. Use this Skill to fine-tune it using human-ranked responses, resulting in a model that better follows instructions and aligns with desired behavior.

Quick Start

Use the fine-tuning-with-trl skill to perform supervised fine-tuning on the 'Qwen/Qwen2.5-0.5B' model using the 'trl-lib/Capybara' 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 a large language model with human preferences?▼

Use this skill when your base LLM generates factually correct but unhelpful or biased responses. Fine-tune it using human-ranked responses to better follow instructions and align with desired behavior.

What is Direct Preference Optimization (DPO) and how does it simplify LLM alignment?▼

DPO aligns models directly using preference data without needing a separate reward model. This simplifies the alignment process compared to full RLHF pipelines by skipping reward model training.

Can I use Proximal Policy Optimization (PPO) for RLHF with HuggingFace Transformers?▼

Yes, you can use Proximal Policy Optimization (PPO) for full RLHF pipelines. The skill supports implementing reward model training and PPO to align base models with human-ranked responses.

How does Group Relative Policy Optimization (GRPO) reduce memory during LLM training?▼

Group Relative Policy Optimization (GRPO) provides memory-efficient reinforcement learning for fine-tuning. It allows training large language models with reduced memory footprints compared to standard PPO.

Do I need PEFT and Accelerate to run TRL reinforcement learning pipelines?▼

You need the TRL library along with HuggingFace Transformers, Datasets, PEFT, Accelerate, and PyTorch. These dependencies integrate to provide comprehensive LLM post-training and environment setup.

When should I use reinforcement learning from human feedback to align my base model?▼

Use this skill when your base LLM generates factually correct but unhelpful or biased responses. Fine-tune it using human-ranked responses to better follow instructions and align with desired behavior.

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