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simpo-training

Optimize LLM preferences with SimPO using chosen versus rejected responses.

Updated May 3, 2026
Search Tags:#preference optimization#simpo#policy optimization#llm alignment#reward learning
One-click install
npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill simpo-training-yangel-hide
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/Yangel-hide/video-production-planner-agent/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill simpo-training-yangel-hide

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

SimPO enables efficient, reference-free preference optimization to align LLMs by directly maximizing the preference signal from chosen vs rejected responses, avoiding the need for a labeled reference model.

Core Features & Use Cases

  • Reference-free optimization: trains with only model outputs and human preferences.
  • Configurable margins: adjusts beta and gamma to control reward strength and decision boundary.
  • End-to-end training workflows: supports dataset preparation, training scripts, and evaluation for alignment tasks.

Quick Start

Launch a SimPO training run using a prepared base model and configuration to begin preference alignment.

Frequently Asked Questions about simpo-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I align an LLM without a reference model for preference optimization?▼

SimPO enables reference-free preference optimization by directly maximizing the log-probability difference between chosen and rejected responses, avoiding the need for a labeled reference model during LLM alignment.

What do I need to set up before starting reference-free preference optimization?▼

Reference-free preference optimization requires a base model, a prepared preference dataset containing chosen and rejected responses, and a training configuration to define beta, gamma, and loss settings.

How do I control the reward strength and decision boundary during SimPO training?▼

You can control reward strength and decision boundary during SimPO training by adjusting configurable beta and gamma margins, which directly manage the preference signal between chosen and rejected responses.

Can I use transformers and torch for LLM alignment tasks with SimPO?▼

Yes, SimPO operates with torch, transformers, datasets, trl, and accelerate to execute end-to-end LLM alignment workflows including dataset preparation, training scripts, and evaluation across instruction-following benchmarks.

Does SimPO support SFT regularization during policy optimization?▼

Yes, SimPO supports optional SFT regularization alongside standard hyperparameters during policy optimization to stabilize training and maintain base model capabilities across reasoning benchmarks.

When should I use reference-free preference optimization instead of standard RLHF?▼

Use reference-free preference optimization when you need efficient LLM alignment without training or loading a separate reference model, directly applying preference signals to instruction-following and reasoning tasks.

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