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fine-tuning-with-trl

Fine-tune large language models with TRL for SFT, DPO, and GRPO workflows.

Updated May 4, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#dpo#trl#rlhf#grpo
One-click install
npx skills add https://github.com/InverterNetwork/hermes-agent --skill fine-tuning-with-trl-inverternetwork
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/InverterNetwork/hermes-agent/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/InverterNetwork/hermes-agent --skill fine-tuning-with-trl-inverternetwork

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of aligning large language models with human preferences, providing a structured path for instruction tuning and reinforcement learning without the need for manual, ad-hoc training scripts.

Core Features & Use Cases

  • Post-Training Pipelines: Execute full RLHF workflows including SFT, Reward Modeling, and RLOO or GRPO.
  • Preference Alignment: Use DPO to align models with chosen/rejected preference pairs efficiently.
  • Use Case: A developer needs to align a base model to follow specific reasoning formats; this skill provides the templates and workflows to implement GRPO with custom reward functions for structured output.

Quick Start

Run the fine-tuning skill to initiate a GRPO training session using the provided basic_grpo_training template on 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 align a large language model with human preferences using reinforcement learning?▼

You align large language models with human preferences by executing post-training workflows like Supervised Fine-Tuning, Direct Preference Optimization, and online reinforcement learning using custom reward functions and preference datasets.

What's the best way to implement DPO for preference alignment?▼

To implement DPO for preference alignment, you align models efficiently using chosen and rejected preference pairs, integrating your preference datasets directly into the training pipeline to achieve specific model behaviors without manual ad-hoc scripts.

Can I use custom reward functions to enforce specific reasoning formats during GRPO training?▼

Yes, you can integrate custom reward functions during GRPO training to enforce specific reasoning formats, facilitating structured output and achieving targeted model behaviors through online reinforcement learning methods.

Does this reinforcement learning framework support full RLHF workflows from SFT to RLOO?▼

Yes, the framework supports full RLHF workflows, allowing you to execute Supervised Fine-Tuning, Reward Modeling, and online reinforcement learning methods like RLOO or GRPO to comprehensively post-train and align models.

Do I need peft and accelerate to run LLM fine-tuning workflows?▼

Yes, these fine-tuning workflows require dependencies including peft, accelerate, transformers, datasets, and torch to properly execute the reinforcement learning pipelines and manage large language model training.

Why use online reinforcement learning methods like RLOO instead of supervised fine-tuning?▼

Use online reinforcement learning methods like RLOO instead of supervised fine-tuning when you need to align models with complex human preferences and custom reward functions, moving beyond simple instruction mimicry to achieve specific output structures.

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