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fine-tuning-with-trl

Fine-tune large language models with TRL using SFT, DPO, PPO, GRPO, and reward modeling.

Updated Jul 3, 2026
Search Tags:#optimization#fine-tuning#reinforcement learning#language model#model alignment
One-click install
npx skills add https://github.com/LynxLabVN/office-agent --skill fine-tuning-with-trl-lynxlabvn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/LynxLabVN/office-agent/tree/main/agent-core/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/LynxLabVN/office-agent --skill fine-tuning-with-trl-lynxlabvn

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 align large language models with human preferences and optimize them for specific tasks through fine-tuning and reinforcement learning techniques.

Core Features & Use Cases

  • Fine-Tuning: Perform supervised fine-tuning, DPO, PPO, GRPO, and reward modeling for LLMs.
  • Reinforcement Learning: Implement reinforcement learning methods to improve model performance.
  • Use Case: Imagine you have a LLM that needs to generate text aligned with specific preferences. Use this Skill to train the model on preference data and achieve the desired alignment.

Quick Start

Run the 'trl' command with the required arguments to train a model on preference data.

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 a large language model with human preferences using reinforcement learning?▼

Align large language models with human preferences by applying reinforcement learning techniques like PPO, DPO, and GRPO. This approach trains models on preference data to optimize responses for specific tasks and desired behaviors.

What's the best way to fine-tune a language model for a specific task?▼

The best way to fine-tune a language model for a specific task is through supervised fine-tuning. This process adapts the model using task-specific datasets and libraries like transformers, trl, and accelerate to achieve targeted optimization.

Do I need peft and accelerate to train large language models with trl?▼

Yes, training large language models with trl typically requires peft and accelerate along with transformers, datasets, and torch. These libraries provide the necessary environment setup for efficient model optimization and reinforcement learning.

What is DPO and how does it compare to PPO for language model alignment?▼

DPO (Direct Preference Optimization) and PPO (Proximal Policy Optimization) are both methods for language model alignment. DPO directly optimizes preferences without a separate reward model, while PPO uses reinforcement learning with reward modeling to guide training.

Can I use supervised fine-tuning and reward modeling in the same training pipeline?▼

Yes, you can combine supervised fine-tuning and reward modeling in the same pipeline. Reward modeling establishes the optimization target, while supervised fine-tuning initializes the model before applying reinforcement learning methods like GRPO or PPO.

Why does language model fine-tuning require preference data for alignment?▼

Language model fine-tuning requires preference data for alignment because reinforcement learning methods need comparative examples to optimize behaviors. This data guides reward modeling and algorithms like DPO to match human preferences effectively.

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