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fine-tuning-with-trl

Fine-tune language models with TRL SFT, DPO, PPO, GRPO, and reward modeling.

Updated Apr 27, 2026
Search Tags:#fine-tuning#ppo#dpo#trl#rlhf#sft#grpo
One-click install
npx skills add https://github.com/x-TheFox/Corvus --skill fine-tuning-with-trl-x-thefox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/x-TheFox/Corvus/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/x-TheFox/Corvus --skill fine-tuning-with-trl-x-thefox

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Fine-tune language models to align them with human preferences using TRL's SFT, DPO, PPO, GRPO, and reward modeling.

Core Features & Use Cases

  • End-to-end TRL workflows for instruction tuning and RLHF.
  • Supports SFT, DPO, PPO, GRPO, and reward modeling across datasets, reward models, and deployments.
  • Real-world use: Align an enterprise assistant to adhere to policies and style.

Quick Start

Install the TRL tooling and dependencies, prepare your dataset, and run the configured training script to begin fine-tuning.

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 RLHF using TRL?▼

Fine-tune language models for RLHF by applying TRL's SFT, DPO, PPO, GRPO, and reward modeling workflows to align outputs with human preferences. You configure training scripts and manage dependencies to execute instruction tuning and preference optimization across datasets.

What is the best way to align an enterprise assistant with specific style policies?▼

Align an enterprise assistant to adhere to policies and style by running end-to-end TRL workflows for instruction tuning and preference optimization. The skill applies SFT and DPO configurations to tailor model behavior to specific enterprise guidelines.

Does TRL support both offline preference optimization and online RL training?▼

TRL supports both offline preference optimization and online RL training through its DPO, PPO, and GRPO implementations. You can configure reward modeling and run online training scenarios across diverse datasets to align language models.

Can I use PEFT and accelerate for TRL fine-tuning workflows?▼

Yes, TRL fine-tuning workflows utilize PEFT and accelerate alongside transformers, datasets, and torch. These dependencies manage training configurations and distributed execution for SFT, DPO, and reward modeling tasks.

When should I use GRPO instead of PPO for language model alignment?▼

Use GRPO or PPO for language model alignment based on your specific reward function implementations and training scenarios. The skill demonstrates diverse reward modeling configurations using common ML tooling to handle various RLHF requirements.

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