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fine-tuning-with-trl

Optimize language models with SFT, DPO, RLOO, and GRPO post-training alignment.

Updated Jun 17, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#dpo#trl#rlhf#grpo
One-click install
npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill fine-tuning-with-trl-cxnaive
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/cxnaive/hermes-agent-llbot/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill fine-tuning-with-trl-cxnaive

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of aligning large language models with human preferences, providing a streamlined path for post-training tasks like instruction tuning and reinforcement learning.

Core Features & Use Cases

  • Post-Training Pipelines: Supports SFT, DPO, RLOO, and GRPO for comprehensive model alignment.
  • Memory-Efficient RL: Implements GRPO for training with minimal VRAM requirements.
  • Use Case: Use this skill to take a base model like Qwen and fine-tune it to follow specific reasoning formats or align it with human-rated preference datasets.

Quick Start

Run the trl rloo command with your base model and reward model paths to initiate the reinforcement learning pipeline.

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 large language model with RLHF and DPO?▼

This skill optimizes language models through post-training alignment techniques including SFT, DPO, RLOO, and GRPO to align outputs with human preferences. It creates reward-driven RLHF pipelines for instruction-following models.

What is the best way to train LLMs with minimal VRAM using GRPO?▼

GRPO implements memory-efficient reinforcement learning to train large language models with minimal VRAM requirements. It enables reward-driven RLHF pipelines by optimizing model alignment without exhausting hardware resources.

Do I need transformers and peft to run RLHF pipelines?▼

Yes, executing RLHF training workflows requires integration with transformers, datasets, peft, and accelerate libraries to process data and manage memory-efficient reinforcement learning for instruction-following models.

Can I use DPO to align a Qwen base model with human preferences?▼

Yes, you can take a base model like Qwen and fine-tune it to follow specific reasoning formats or align it with human-rated preference datasets using DPO and other post-training alignment pipelines.

How do I start a reinforcement learning pipeline with a reward model?▼

To start a reinforcement learning pipeline, run the trl rloo command with your base model and reward model paths. This initiates the RLOO training workflow for aligning your language model.

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