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fine-tuning-with-trl

Automates LLM post-training alignment with TRL using SFT, DPO, and PPO/GRPO workflows.

1|Updated May 16, 2026
Search Tags:#fine-tuning#ppo#dpo#trl#rlhf#grpo#preference alignment
One-click install
npx skills add https://github.com/devMoez/titan --skill fine-tuning-with-trl-devmoez
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/devMoez/titan --skill fine-tuning-with-trl-devmoez

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines post-training alignment of language models by providing practical, end-to-end workflows for supervised fine-tuning and preference/RL-based optimization using TRL.

Core Features & Use Cases

  • Instruction tuning (SFT): Train a base model on prompt–completion data to improve task-following.
  • Preference alignment (DPO): Optimize directly from chosen/rejected pairs to better match human or rubric preferences.
  • Reinforcement learning alignment (PPO / GRPO): Use reward modeling (PPO) or online group-relative rewards (GRPO) to improve outputs with custom reward signals.
  • Common training workflows: Full RLHF pipeline (SFT → reward model → PPO) and lighter-weight alternatives (DPO-only, or GRPO with reward functions).
  • Use case: Improve a 7B instruct model so it reliably follows a strict XML/JSON output format while also maximizing correctness on verifiable tasks (e.g., math or QA with ground truth).

Quick Start

Use the TRL flow to fine-tune your model with SFT or DPO by running the provided Python snippets against your 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 LLMs with SFT, DPO, and PPO using TRL?▼

You can fine-tune LLMs using TRL by applying supervised fine-tuning for instruction tuning, DPO for preference optimization, and PPO for reinforcement learning alignment with custom reward models.

What's the best way to align a model to output strict JSON or XML formats?▼

To align a model for strict JSON or XML outputs, use reinforcement learning alignment with custom reward functions via GRPO or PPO to optimize outputs against verifiable task rewards.

Do I need PEFT to run DPO or PPO workflows on a 7B model?▼

PEFT is supported to enable memory efficiency during DPO or PPO workflows, making it highly suitable for training 7B models with reduced VRAM requirements.

How does GRPO differ from PPO for preference alignment?▼

GRPO uses online group-relative rewards for optimization, whereas PPO relies on a trained reward model, allowing GRPO to optimize directly from custom reward functions without a separate reward model.

Can I optimize a base model directly from chosen and rejected response pairs?▼

Yes, you can optimize a base model directly from chosen and rejected pairs using Direct Preference Optimization (DPO) to align outputs with human or rubric preferences.

What dependencies are required to run RLHF workflows with TRL?▼

Running RLHF workflows requires TRL, Transformers, Datasets, PEFT, Accelerate, and PyTorch to handle training, dataset processing, and memory-efficient fine-tuning.

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