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fine-tuning-with-trl

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

4|Updated Apr 19, 2026
Search Tags:#fine-tuning#TRL#RLHF#language model#reforcement learning
One-click install
npx skills add https://github.com/ragnarokhaa/hermes --skill fine-tuning-with-trl-ragnarokhaa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/ragnarokhaa/hermes/tree/main/hermes-cerul-tech-news-package/hermes-cerul-tech-news-package/hermes-agent/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/ragnarokhaa/hermes --skill fine-tuning-with-trl-ragnarokhaa

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 solves the challenge of fine-tuning large language models to align with human preferences and perform specific tasks, enabling advanced language model training.

Core Features & Use Cases

  • Reinforcement Learning with TRL: Utilizes Transformer Reinforcement Learning (TRL) for fine-tuning LLMs with SFT, DPO, PPO, and GRPO methods.
  • RLHF Pipeline: Offers a complete RLHF pipeline for aligning models with human feedback.
  • Use Case: Ideal for scenarios where you need to train an LLM to perform specific tasks, such as generating creative content, answering technical questions, or translating text.

Quick Start

Install the required packages and run the following command to fine-tune an LLM using TRL:

python -m trl.scripts.ppo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct --reward_model_path reward-model --dataset_name trl-internal-testing/descriptiveness-sentiment-trl-style --output_dir model-ppo --learning_rate 3e-6 --per_device_train_batch_size 64 --total_episodes 10000

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 using reinforcement learning with TRL?▼

You can fine-tune a large language model using reinforcement learning with TRL by running the provided scripts for SFT, DPO, PPO, or GRPO to align the model with human preferences. It provides a complete pipeline for advanced training tasks.

What is the difference between SFT, DPO, PPO, and GRPO for RLHF?▼

SFT, DPO, PPO, and GRPO are distinct methods for reinforcement learning with human feedback. SFT performs supervised fine-tuning, while DPO, PPO, and GRPO optimize models directly against reward models or preference data to align outputs with human preferences.

Do I need specific Python packages to run TRL fine-tuning scripts?▼

Yes, you need specific Python packages to run TRL fine-tuning scripts. The required dependencies include trl, transformers, datasets, peft, accelerate, and torch to properly execute the reinforcement learning pipeline.

Can I use TRL to train an LLM for specific tasks like content generation?▼

Yes, you can use TRL to train an LLM for specific tasks like content generation. It is ideal for scenarios where you need to train a model to perform specific tasks such as generating creative content, answering technical questions, or translating text.

What is the best way to start a PPO training run for an instruct model?▼

The best way to start a PPO training run for an instruct model is using the trl.scripts.ppo command. You specify the model path, reward model, dataset, output directory, learning rate, batch size, and total episodes to begin training.

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