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fine-tuning-with-trl

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

1|Updated Feb 10, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#trl#rlhf#preference alignment
One-click install
npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill fine-tuning-with-trl-informatico-madrid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/informatico-madrid/Architect-Expert-Gap-Forge/tree/main/.github/skills/trl-fine-tuning
Command: npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill fine-tuning-with-trl-informatico-madrid

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of aligning Large Language Models (LLMs) with human preferences and instructions, moving beyond basic pre-training to create more helpful, harmless, and honest AI assistants.

Core Features & Use Cases

  • Reinforcement Learning from Human Feedback (RLHF): Implements advanced techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and Group Relative Policy Optimization (GRPO).
  • Preference Alignment: Trains models to understand and adhere to user preferences, ensuring outputs are more desirable and aligned with human values.
  • Reward Model Training: Enables the creation of reward models that can score LLM outputs, crucial for RL-based alignment.
  • Use Case: Fine-tune a base LLM to generate more creative stories, provide safer and more ethical responses, or follow complex instructions more accurately by leveraging human feedback data.

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 align large language models with human preferences using RLHF?▼

You can align large language models with human preferences by applying reinforcement learning techniques like Supervised Fine-Tuning, Direct Preference Optimization, and Proximal Policy Optimization to adjust model outputs based on human feedback data.

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

Direct Preference Optimization trains models directly on preference data without a separate reward model, while Proximal Policy Optimization uses a trained reward model to score outputs and update the policy during reinforcement learning.

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

Yes, LLM fine-tuning with TRL supports integration with PEFT for parameter-efficient fine-tuning and accelerate for distributed training, working alongside PyTorch and Transformers to manage large model workloads efficiently.

How do I train a reward model to score LLM outputs?▼

Reward model training involves using preference datasets to teach a model to score LLM outputs based on human preferences, creating a reward signal necessary for running Proximal Policy Optimization during reinforcement learning.

Do I need PyTorch and Transformers to run DPO and SFT workflows?▼

Yes, PyTorch and Transformers are required dependencies for running Supervised Fine-Tuning and Direct Preference Optimization workflows, providing the foundational model architectures and tensor operations needed for training.

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