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fine-tuning-with-trl

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

1|Updated Mar 22, 2026
Search Tags:#fine-tuning#datasets#transformers#rl#trl#rlhf#instruction-tuning
One-click install
npx skills add https://github.com/nelohenriq/hermes-agent-plus --skill fine-tuning-with-trl-nelohenriq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/nelohenriq/hermes-agent-plus/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/nelohenriq/hermes-agent-plus --skill fine-tuning-with-trl-nelohenriq

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fine-tune language models using reinforcement learning with TRL to align outputs with human preferences and improve task performance.

Core Features & Use Cases

  • SFT instruction tuning with TRL for instruction-following capabilities.
  • Preference alignment via DPO, PPO, and GRPO workflows.
  • Reward-model training to support end-to-end RLHF pipelines.
  • Works with HuggingFace Transformers and common datasets for various domains.

Quick Start

Provide your base model and a dataset, then run the TRL fine-tuning pipeline to produce an aligned model.

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 a language model for human preference alignment?▼

You fine-tune language models for human preference alignment by applying reinforcement learning using TRL workflows like DPO, PPO, and GRPO. This adapts model outputs to align with specific human preferences.

What is the difference between SFT and DPO in TRL?▼

SFT, or supervised fine-tuning, trains instruction-following capabilities, while DPO, or direct preference optimization, performs preference alignment. Both are TRL components used to fine-tune language models.

How do I train a reward model for an RLHF pipeline?▼

You train a reward model to support end-to-end RLHF pipelines using TRL. This provides the necessary reward signals for reinforcement learning fine-tuning with PPO or GRPO workflows.

Do I need to install transformers and datasets to use TRL for instruction tuning?▼

Yes, you need the TRL library and related dependencies such as transformers, datasets, peft, accelerate, and torch to perform SFT instruction tuning and preference alignment workflows.

Can I use TRL with HuggingFace models for online RL workflows?▼

Yes, TRL works with HuggingFace Transformers and common datasets to run online RL workflows. You provide your base model and a dataset to run the fine-tuning pipeline and produce an aligned model.

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