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fine-tuning-with-trl

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

11.5k|842|Updated Nov 3, 2025
Search Tags:#llm#fine-tuning#alignment#reinforcement learning#ppo#dpo#trl
One-click install
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill fine-tuning-with-trl-orchestra-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/06-post-training/trl-fine-tuning
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill fine-tuning-with-trl-orchestra-research

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 streamlines the process of aligning Large Language Models (LLMs) with human preferences and instructions, making them more helpful, harmless, and honest.

Core Features & Use Cases

  • Instruction Tuning (SFT): Fine-tune models to follow instructions effectively.
  • Preference Alignment (DPO): Align models with human preferences using direct preference optimization.
  • Reinforcement Learning (PPO/GRPO): Optimize models using reward signals for advanced alignment.
  • Reward Model Training: Train models to score the quality of generated text.
  • 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 feedback data to produce responses that are both accurate and aligned 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 provided 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 using reinforcement learning?▼

Fine-tune Large Language Models with human preferences using TRL by applying Proximal Policy Optimization or Direct Preference Optimization to align base models with human feedback data, making responses more helpful and harmless.

What is the difference between SFT, DPO, and PPO for LLM alignment?▼

SFT performs instruction tuning to follow commands, DPO aligns models directly with preference data, and PPO optimizes models using reward signals for advanced reinforcement learning alignment, all supported by the TRL framework.

Can I use PEFT and accelerate for memory-efficient LLM instruction tuning?▼

Yes, supervised fine-tuning and preference alignment integrate with PEFT and accelerate to optimize memory usage during training, working seamlessly with HuggingFace Transformers and Datasets for efficient model updates.

How do I train a reward model to score generated text quality?▼

Train a reward model to score generated text quality using reinforcement learning techniques provided by the TRL library, which handles the optimization process to evaluate and rank output responses based on reward signals.

What's the best way to align a base LLM that gives factually correct but biased responses?▼

Align biased base LLMs by applying Direct Preference Optimization or Group Relative Policy Optimization through TRL, using human feedback data to fine-tune the model so it produces responses that are both accurate and behaviorally aligned.

Do I need PyTorch and HuggingFace Transformers to run DPO and GRPO training?▼

Yes, PyTorch, HuggingFace Transformers, and Datasets are required dependencies to execute DPO and GRPO training, providing the foundational deep learning and data processing environment for reinforcement learning workflows.

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