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fine-tuning-with-trl

Fine-tune language models with TRL methods for human preference alignment.

6|3|Updated Jan 29, 2026
Search Tags:#fine-tuning#TRL#RLHF#language model#reforcement learning
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill fine-tuning-with-trl-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/trl-fine-tuning
Command: npx skills add https://github.com/jonnabio/ace-framework --skill fine-tuning-with-trl-jonnabio

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 to improve language models by fine-tuning them with advanced reinforcement learning methods and TRL (Transformer Reinforcement Learning) techniques, making them more aligned with human preferences and instructions.

Core Features & Use Cases

  • Advanced Fine-Tuning: Supports fine-tuning using SFT (Supervised Fine-Tuning), DPO (Direct Preference Optimization), PPO (Proximal Policy Optimization), and GRPO (Group Relative Policy Optimization).
  • Reward Modeling: Includes training reward models to predict human preferences for reinforcement learning.
  • Use Case: For developers who need to align their language models with human feedback, or to train models from human preferences, this Skill offers a comprehensive toolkit for advanced reinforcement learning tasks.

Quick Start

To start fine-tuning your model, install the required dependencies and run the following commands:

  • Install dependencies: 
    pip install trl transformers datasets peft accelerate torch
  • Perform Supervised Fine-Tuning (SFT): 
    from trl import SFTTrainer
trainer = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,
)
trainer.train()

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 language model with human preferences using reinforcement learning?▼

You align a language model with human preferences using reinforcement learning by applying TRL methods like SFT, DPO, PPO, or GRPO to fine-tune the model based on human feedback datasets.

What is the difference between SFT, DPO, PPO, and GRPO for fine-tuning LLMs?▼

SFT performs supervised fine-tuning on instruction data, while DPO, PPO, and GRPO apply preference optimization techniques using reward models or direct human preference data to align LLMs.

Can I use HuggingFace Transformers with TRL for reward modeling?▼

Yes, you can use HuggingFace Transformers with TRL for reward modeling, as TRL provides built-in support to train reward models that predict human preferences for reinforcement learning tasks.

How do I start supervised fine-tuning with TRL in Python?▼

To start supervised fine-tuning with TRL, install the required dependencies and initialize the SFTTrainer with your base model and dataset, then call the train method to begin the process.

What dependencies do I need to install for TRL reinforcement learning fine-tuning?▼

You need to install trl, transformers, datasets, peft, accelerate, and torch to perform reinforcement learning fine-tuning and align language models with human preferences effectively.

When should I use Direct Preference Optimization instead of PPO for LLM training?▼

Use Direct Preference Optimization instead of PPO when you want to align LLMs directly from human preference data without explicitly training a separate reward model during the reinforcement learning process.

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