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simpo-training

Automate reference-free preference optimization for aligning LLMs with human feedback.

1|1|Updated Apr 26, 2026
Search Tags:#nlp#training#simpo#reference-free#preference-optimization#llm-alignment
One-click install
npx skills add https://github.com/BermudaLocals/hermes-agent-lite --skill simpo-training-bermudalocals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/BermudaLocals/hermes-agent-lite/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/BermudaLocals/hermes-agent-lite --skill simpo-training-bermudalocals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SimPO provides a reference-free preference optimization method for aligning LLMs with human feedback, enabling efficient improvements without a dedicated reference model.

Core Features & Use Cases

  • Reference-free alignment: directly optimizes preference signals from prompts and model outputs.
  • Configurable training workflows: supports base, instruct, and chat-style models with common datasets like UltraFeedback and Distilabel-Math.
  • Reproducible experimentation: includes end-to-end configs and tooling to reproduce results across runs.

Quick Start

Run the provided training script with a ready-made config to start SimPO training on your base model.

Frequently Asked Questions about simpo-training

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

FAQPage Schema
What is reference-free preference optimization for LLM alignment?▼

Reference-free preference optimization aligns LLMs with human feedback by directly optimizing preference signals from prompts and model outputs, eliminating the need for a separate reference model during training.

How do I align a base or chat model using SimPO training?▼

You can align base, instruct, or chat models by running the provided training script with a ready-made config, using preference datasets like UltraFeedback or Distilabel-Math to refine the model policy.

Can I use SimPO for preference alignment without a dedicated reference model?▼

Yes, SimPO directly optimizes preference signals from prompts and model outputs, allowing efficient LLM alignment and policy refinement without the computational overhead of loading a dedicated reference model.

What datasets work with reference-free preference optimization workflows?▼

Reference-free preference optimization workflows support common human feedback datasets like UltraFeedback and Distilabel-Math for evaluating model outputs and refining alignment policies.

How do I reproduce LLM alignment results across multiple training runs?▼

You can reproduce experimentation results across runs by using the included end-to-end configurations and tooling, which ensure consistent preference alignment training and policy evaluation outputs.

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