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fine-tuning-with-trl

Fine-tune language models with TRL for preference alignment and reinforcement learning.

539|39|Updated May 1, 2026
Search Tags:#fine-tuning#reinforcement learning#transformer#language models#preference alignment
One-click install
npx skills add https://github.com/Tommy-yw/RunbookHermes --skill fine-tuning-with-trl-tommy-yw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Tommy-yw/RunbookHermes/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/Tommy-yw/RunbookHermes --skill fine-tuning-with-trl-tommy-yw

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 helps fine-tune language models using Transformer Reinforcement Learning (TRL) methods, addressing the need for aligning models with human preferences, optimizing rewards, and improving model performance through reinforcement learning techniques.

Core Features & Use Cases

  • Supervised Fine-Tuning (SFT): Train models on input-output pairs for instruction following and task-specific fine-tuning.
  • Direct Preference Optimization (DPO): Align models with human preferences using preference data without a separate reward model.
  • Group Relative Policy Optimization (GRPO): Perform online reinforcement learning with minimal memory usage, optimizing policies using reward functions.
  • Reward Modeling: Train reward models to score completions based on human preferences for PPO and GRPO training.
  • Use Cases: Ideal for AI research, model refinement, and real-world applications like chatbots, content generation, and task automation.

Quick Start

Run the following command to start the fine-tuning process:

python train_trl.py

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 language models for preference alignment using TRL?▼

You can fine-tune language models for preference alignment by running the TRL training script. It supports methods like DPO and GRPO to optimize models based on human preference data.

What's the difference between DPO and GRPO for reinforcement learning in transformers?▼

DPO aligns models directly using preference data without a separate reward model, while GRPO performs online reinforcement learning with minimal memory usage by optimizing policies through reward functions.

Do I need PEFT and accelerate to train a reward model with TRL?▼

Yes, training reward models with TRL requires the peft and accelerate libraries alongside transformers, datasets, and torch to properly handle the training and alignment processes.

Can I use supervised fine-tuning for instruction following before applying direct preference optimization?▼

Yes, you can apply Supervervised Fine-Tuning to train models on input-output pairs for instruction following, then use Direct Preference Optimization to align the model with human preferences.

What dependencies are required to run TRL fine-tuning scripts?▼

Running TRL fine-tuning scripts requires the trl, transformers, datasets, peft, accelerate, and torch dependencies to be installed in your environment.

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