fine-tuning-with-trl

Combine supervised fine-tuning and reinforcement learning with TRL for model alignment.

6|Updated Apr 26, 2026

Search Tags:#pytorch #transformers #reinforcement-learning #peft #rl #trl #rlhf

Ready to run

npx skills add https://github.com/Strategic-Automation/arachne --skill fine-tuning-with-trl-strategic-automation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Strategic-Automation/arachne/tree/main/src/arachne/skills/default/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/Strategic-Automation/arachne --skill fine-tuning-with-trl-strategic-automation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tune LLMs using TRL-based RLHF pipelines to align AI behavior with human preferences, replacing brittle prompt-chaining with structured, reward-driven optimization.

Core Features & Use Cases

SFT, DPO, PPO, and GRPO workflows for end-to-end alignment with reinforcement learning.
Reward modeling, policy optimization, and memory-efficient configurations for production-grade fine-tuning.
Use case: teams building instruction-following agents can accelerate alignment with safer, auditable training loops.

Quick Start

Provide a minimal, ready-to-run TRL RLHF fine-tuning example to begin customizing a 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 LLMs with RLHF using TRL and PyTorch?▼

To fine-tune LLMs with RLHF using TRL, you combine supervised fine-tuning and reinforcement learning to align models with human preferences. The workflow applies SFT, DPO, PPO, and GRPO techniques to build safer, more capable instruction-following agents.

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

SFT, DPO, PPO, and GRPO are distinct preference alignment and reward-modeling workflows within TRL. They provide structured, reward-driven optimization to align AI behavior with human preferences, replacing brittle prompt-chaining with auditable training loops for instruction-following agents.

Can I use PEFT and Accelerate for memory-efficient training with TRL?▼

Yes, TRL supports memory-efficient training configurations using PEFT and Accelerate alongside Transformers, Datasets, and PyTorch. This enables production-grade fine-tuning and policy optimization for large language models without exceeding hardware memory constraints.

Does TRL support flexible data formats and diverse reward signals for reinforcement learning?▼

Yes, TRL supports flexible data formats and diverse reward signals for reinforcement learning. This allows you to apply reward modeling and policy optimization across SFT, DPO, PPO, and GRPO workflows to achieve precise preference alignment for your agents.

What's the best way to start building instruction-following agents with human preference alignment?▼

The best way to build instruction-following agents is using TRL-based RLHF pipelines for human preference alignment. You can accelerate alignment with safer, auditable training loops by applying SFT, DPO, PPO, and GRPO workflows tailored to your reward signals.