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fine-tuning-with-trl

Fine-tune LLMs with TRL for SFT, DPO, PPO/GRPO, and reward-model training.

Updated Mar 25, 2026
Search Tags:#fine-tuning#pytorch#huggingface#transformers#reinforcement-learning#trl#rlhf
One-click install
npx skills add https://github.com/afel6/scal-ai-pipeline --skill fine-tuning-with-trl-afel6
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/afel6/scal-ai-pipeline/tree/main/hermes_skills_library/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/afel6/scal-ai-pipeline --skill fine-tuning-with-trl-afel6

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Fine-tune LLMs using TRL-based reinforcement learning to align models with human preferences and improve instruction following.

Core Features & Use Cases

  • Supports SFT for instruction tuning
  • DPO-based direct preference alignment
  • PPO/GRPO online RL with reward-model training
  • Works with HuggingFace Transformers and datasets for end-to-end RLHF workflows
  • Use case: customize a base model for domain-specific tasks with human feedback and evaluation

Quick Start

Run a TRL-based fine-tuning workflow on your model to align it with human preferences using SFT, DPO, PPO/GRPO, and reward-model training.

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 RLHF and align them with human preferences?▼

Fine-tune LLMs using TRL-based reinforcement learning to align models with human preferences. The workflow supports SFT, DPO, PPO/GRPO online RL, and reward-model training for end-to-end RLHF pipelines.

Can I use DPO and PPO for direct preference optimization with HuggingFace Transformers?▼

Yes, DPO and PPO are supported for direct preference optimization. The TRL suite integrates with HuggingFace Transformers to execute these reinforcement learning workflows for aligning base models.

What dependencies do I need to run a TRL fine-tuning pipeline?▼

Running a TRL fine-tuning pipeline requires the TRL suite, transformers, datasets, peft, accelerate, and torch. These dependencies execute production-grade reinforcement learning workflows.

How does supervised fine-tuning work for instruction tuning a base model?▼

Supervised fine-tuning (SFT) is supported for instruction tuning a base model. It serves as the initial step in the end-to-end RLHF workflow before applying preference alignment techniques.

Is GRPO supported for online reinforcement learning in TRL?▼

Yes, GRPO is supported for online reinforcement learning alongside PPO. The TRL suite facilitates these online RL methods and reward-model training to customize models for domain-specific tasks.

What is the best way to customize a base model for domain-specific instruction following?▼

The best way to customize a base model for domain-specific tasks is using TRL-based reinforcement learning. It leverages human feedback and evaluation through SFT, DPO, and PPO/GRPO workflows.

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