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fine-tuning-with-trl

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

2.8k|332|Updated Jan 29, 2026
Search Tags:#llm#fine-tuning#transformers#peft#trl#rlhf#grpo
One-click install
npx skills add https://github.com/moltis-org/moltis --skill fine-tuning-with-trl-moltis-org
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/moltis-org/moltis/tree/main/crates/skills/src/assets/mlops/training/fine-tuning-with-trl
Command: npx skills add https://github.com/moltis-org/moltis --skill fine-tuning-with-trl-moltis-org

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, datasets, transformers, peft, trl.

What problem does it solve?

Fine-tune language models efficiently using reinforcement learning with TRL to align outputs with human preferences.

Core Features & Use Cases

  • Supports SFT, DPO, PPO/GRPO workflows for instruction-tuning and preference alignment.
  • Includes guidance on reward-model training and PEFT/LoRA integration.
  • Supports integration with HuggingFace Transformers and common RLHF pipelines.

Quick Start

Provide a ready-to-run TRL-based fine-tuning setup for your language model with SFT/DPO/PPO/GRPO, including optional LoRA integration.

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 reinforcement learning using TRL?▼

You can fine-tune an LLM with reinforcement learning using TRL by running SFT, DPO, PPO, or GRPO workflows to align model outputs with human preferences. The setup integrates HuggingFace Transformers and supports configurable hyperparameters for GPU-based training.

What is the difference between SFT, DPO, and PPO workflows for preference alignment?▼

SFT, DPO, and PPO are distinct TRL workflows for preference alignment: SFT handles instruction-tuning, DPO directly optimizes preferences without a separate reward model, and PPO uses reinforcement learning to optimize outputs against a trained reward model.

Can I use LoRA and PEFT integration to reduce GPU memory during TRL fine-tuning?▼

Yes, you can use LoRA and PEFT integration during TRL fine-tuning to efficiently adapt large language models. This setup provides optional parameter-efficient fine-tuning to reduce computational overhead across instruction-following and reward-model training workflows.

Does TRL support reward-model training for RLHF pipelines?▼

Yes, TRL supports reward-model training for RLHF pipelines. It includes guidance for training reward models and integrates with HuggingFace Transformers to align language model outputs with human preferences through reinforcement learning.

What dependencies do I need to set up a TRL fine-tuning environment?▼

You need torch, datasets, transformers, peft, and trl installed to set up a TRL fine-tuning environment. These dependencies provide the foundational framework for loading datasets, running HuggingFace Transformers, and executing PEFT-integrated RLHF workflows.

When should I use GRPO instead of PPO for LLM fine-tuning?▼

You should use GRPO as an alternative TRL workflow to PPO for LLM fine-tuning when aligning outputs with human preferences. Both are supported reinforcement learning methods within the TRL setup for configurable GPU-based training.

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