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fine-tuning-with-trl

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

Updated Jan 30, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#trl#rlhf#preference alignment
One-click install
npx skills add https://github.com/gagan114662/content_books --skill fine-tuning-with-trl-gagan114662
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/gagan114662/content_books/tree/main/AI-research-SKILLs/06-post-training/trl-fine-tuning
Command: npx skills add https://github.com/gagan114662/content_books --skill fine-tuning-with-trl-gagan114662

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables the fine-tuning of Large Language Models (LLMs) to align their behavior with human preferences and instructions, moving beyond basic pre-training.

Core Features & Use Cases

  • Reinforcement Learning from Human Feedback (RLHF): Fine-tune models using techniques like PPO and GRPO.
  • Preference Alignment: Directly align models with desired outputs using Direct Preference Optimization (DPO).
  • Instruction Tuning: Improve instruction-following capabilities with Supervised Fine-Tuning (SFT).
  • Reward Model Training: Train models to score the quality of LLM generations.
  • Use Case: You have a base LLM and want it to be more helpful, harmless, and honest. You can use SFT to teach it instructions, then train a reward model on human-ranked responses, and finally use PPO or DPO to fine-tune the LLM to maximize the reward score, making it better aligned with human values.

Quick Start

Use the fine-tuning-with-trl skill to fine-tune a model using DPO with the provided preference 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 fine-tune an LLM with human preferences using RLHF?▼

To fine-tune an LLM with RLHF, use TRL to apply Supervised Fine-Tuning (SFT), train a reward model on human-ranked responses, and optimize the model with PPO or GRPO.

What is the best way to align LLM outputs with Direct Preference Optimization (DPO)?▼

Direct Preference Optimization (DPO) aligns LLM outputs by directly optimizing the model against a preference dataset, bypassing the need to train a separate reward model.

Does this fine-tuning Skill work with HuggingFace Transformers and PyTorch?▼

Yes, this fine-tuning Skill operates natively with HuggingFace Transformers, PyTorch, and datasets, and integrates with PEFT and accelerate for efficient model training.

Can I train a reward model to score LLM generations?▼

Yes, you can train a reward model using this Skill to score the quality of LLM generations, which is a required step before applying PPO or GRPO for preference alignment.

When should I use SFT compared to PPO for instruction tuning?▼

Use SFT to initially teach an LLM instruction-following capabilities, and use PPO afterward to fine-tune the model to maximize reward scores based on human preferences.

Why do I need PEFT and accelerate for reinforcement learning on Large Language Models?▼

PEFT and accelerate are required dependencies to efficiently manage memory and distribute workloads when running reinforcement learning fine-tuning pipelines on Large Language Models.

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