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fine-tuning-with-trl

Automate end-to-end language model fine-tuning with TRL-based RLHF workflows.

Updated Apr 16, 2026
Search Tags:#pytorch#huggingface#reinforcement-learning#finetuning#trl#rlhf#instruction-tuning
One-click install
npx skills add https://github.com/jacardl/New-Radar --skill fine-tuning-with-trl-jacardl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/jacardl/New-Radar/tree/main/backend/frameworks/hermes-agent/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/jacardl/New-Radar --skill fine-tuning-with-trl-jacardl

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Automates end-to-end fine-tuning of language models using TRL-based reinforcement learning workflows.

Core Features & Use Cases

  • Supports SFT for instruction tuning
  • Enables DPO, PPO, GRPO, and reward-model training for preference alignment
  • Applicable to a range of model sizes and domains (LLMs, chatbots, and assistants)

Quick Start

Run a TRL-based RLHF fine-tuning pipeline on your chosen model with SFT, DPO, PPO/GRPO, and reward modeling using a TRL-compatible 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 TRL using reinforcement learning and human feedback?▼

You can fine-tune LLMs with TRL by automating end-to-end reinforcement learning workflows. This process requires TRL-compatible datasets and configurations to drive SFT, DPO, PPO, and GRPO pipelines for instruction tuning and human feedback alignment.

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

SFT handles instruction tuning, while DPO, PPO, and GRPO enable preference alignment. These TRL-based reinforcement learning workflows train reward models to align language models with human feedback across various chatbot and assistant domains.

Can I use PEFT and accelerate for RLHF fine-tuning pipelines?▼

Yes, RLHF fine-tuning pipelines require dependencies like PEFT and accelerate alongside TRL, transformers, datasets, and torch. These frameworks support training across a range of model sizes and domains for instruction tuning.

What's the best way to train a reward model for instruction tuning with Huggingface?▼

The best way to train a reward model for instruction tuning is using TRL-based reinforcement learning workflows. This automates reward-model training and preference alignment using TRL-compatible datasets and Huggingface transformers.

Does this RLHF pipeline support different model sizes and domains?▼

Yes, TRL-based RLHF fine-tuning is applicable to a range of model sizes and domains. It effectively supports instruction tuning and human feedback alignment for LLMs, chatbots, and assistants.

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