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simpo-training

Align language models with user preferences using reference-free optimization.

Updated Jun 28, 2026
Search Tags:#datasets#reinforcement-learning#simpo#reference-free#preference-optimization#training-config#llm-alignment
One-click install
npx skills add https://github.com/jleechanorg/hermes-agent --skill simpo-training-jleechanorg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/jleechanorg/hermes-agent/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/jleechanorg/hermes-agent --skill simpo-training-jleechanorg

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Align language models with user preferences using a reference-free optimization approach.

Core Features & Use Cases

  • Reference-free optimization enables efficient alignment without a separate reward or reference model.
  • Flexible workflows support training, fine-tuning, and evaluation with diverse preference datasets.
  • Practical use cases include improving instruction-following and reasoning quality for LLMs in research and product settings.

Quick Start

Run a single SimPO training job using the included example config to begin alignment without a reference model.

Frequently Asked Questions about simpo-training

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

FAQPage Schema
How do I align language models with user preferences without a reference model?▼

SimPO enables preference optimization for language models without a reference model. It aligns LLMs with user preferences using a reference-free approach, eliminating the need for a separate reward or reference model during training.

What hyperparameters can I configure for reference-free preference optimization?▼

Configurable hyperparameters for reference-free preference optimization include learning_rate, beta, gamma_beta_ratio, sft_weight, and loss_type. These allow you to control the training and fine-tuning workflows for aligning LLMs.

Can I use diverse preference datasets for LLM alignment training?▼

Flexible workflows support training, fine-tuning, and evaluating LLM alignment with diverse preference datasets. This is applicable in research and development settings to improve instruction-following and reasoning quality.

Does reference-free preference optimization work for improving instruction-following in LLMs?▼

Reference-free preference optimization improves instruction-following and reasoning quality for LLMs. It provides practical use cases for enhancing model behavior in both research and product development settings.

How do I start a reference-free preference optimization training job?▼

Run a single SimPO training job using the included example config to begin alignment without a reference model. This framework-friendly workflow allows you to start training and evaluating preferences quickly.

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