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simpo-training

Automate reference-free preference optimization for LLM alignment training.

Updated Aug 27, 2026
Search Tags:#fine-tuning#machine-learning#simpo#reference-free#preference-optimization#llm-alignment#dpo-alternative
One-click install
npx skills add https://github.com/kotakbiasa/hermes-agent --skill simpo-training-kotakbiasa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/kotakbiasa/hermes-agent/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/kotakbiasa/hermes-agent --skill simpo-training-kotakbiasa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SimPO addresses the need for efficient, reference-free alignment of LLMs by optimizing preferences directly without a separate reference model.

Core Features & Use Cases

  • Reference-free preference optimization enables faster, simpler alignment workflows.
  • Supports training and fine-tuning across common model sizes using standard alignment datasets such as Ultrafeedback.
  • Includes configurable hyperparameters (learning_rate, beta, gamma_beta_ratio) and optional SFT regularization to balance alignment with capability preservation.

Quick Start

Run the SimPO training pipeline on your chosen model and dataset to start 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 my LLM using preference optimization without a reference model?▼

Reference-free preference optimization aligns LLMs directly using human preferences without a separate reference model. SimPO automates this by configuring learning rate, beta, and loss type to optimize preferences across various model sizes and datasets.

What is the difference between SimPO and DPO for LLM fine-tuning?▼

SimPO is a reference-free alternative to DPO. While DPO requires a separate reference model during preference optimization, SimPO simplifies the alignment workflow by optimizing preferences directly, resulting in faster and simpler fine-tuning.

Can I use standard alignment datasets for reference-free LLM training?▼

Yes, reference-free preference optimization supports standard alignment datasets like Ultrafeedback. The workflow includes built-in dataset handling to apply human preferences across common model sizes during training and fine-tuning.

How do I configure hyperparameters for preference optimization?▼

You can configure preference optimization by adjusting hyperparameters such as learning rate, beta, gamma_beta_ratio, loss_type, and sft_weight. Optional SFT regularization helps balance alignment with capability preservation during fine-tuning.

Does preference optimization without a reference model preserve model capabilities?▼

Reference-free preference optimization includes optional SFT regularization controlled by the sft_weight parameter. This balances LLM alignment with capability preservation during fine-tuning across different model sizes and alignment datasets.

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