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simpo-training

Train language models from chosen/rejected preference pairs without a reference model.

Updated Aug 27, 2026
Search Tags:#distributed training#preference optimization#llm alignment#dpo alternative#reference-free#chosen rejected#sft regularization
One-click install
npx skills add https://github.com/big4council-prog/b4c-agent --skill simpo-training-big4council-prog
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/big4council-prog/b4c-agent/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/big4council-prog/b4c-agent --skill simpo-training-big4council-prog

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SimPO solves inefficient and reference-model-dependent preference alignment by enabling reference-free training from chosen/rejected pairs, so you can get better aligned behavior without the DPO-style setup overhead.

Core Features & Use Cases

  • Reference-free preference optimization: Trains directly from preference pairs (chosen vs rejected) without requiring π_ref or a separate reference model.
  • SimPO loss with controllable preference margin: Uses β and gamma_beta_ratio to scale reward signal and enforce a target margin in logit space.
  • Optionally preserve capabilities with SFT regularization: Adds an SFT component via sft_weight to reduce forgetting, especially when starting from instruct/chat models.
  • Use Case: If you have an UltraFeedback-style dataset of (prompt, chosen, rejected) pairs and want to improve instruction-following, you can fine-tune a Mistral or Llama model using SimPO to push chosen responses above rejected ones.

Quick Start

Use the simpo-training skill to fine-tune a model on a chosen/rejected preference dataset by running SimPO training with a config like training_configs/mistral-7b-base-simpo.yaml.

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 with chosen and rejected preference pairs without a reference model?▼

SimPO enables reference-free preference optimization by training directly on chosen/rejected pairs, eliminating the need for a separate reference model like π_ref used in DPO setups.

Do I need a separate reference model for preference optimization like DPO?▼

No, SimPO is a reference-free preference optimization method that trains directly from chosen/rejected pairs, eliminating the DPO-style setup overhead of loading a separate π_ref reference model.

How do I fine-tune a Mistral or Llama model on an UltraFeedback preference dataset?▼

You can fine-tune Mistral or Llama models on UltraFeedback-style prompt/chosen/rejected datasets using SimPO training with a configuration file like training_configs/mistral-7b-base-simpo.yaml.

Can I prevent capability forgetting during reference-free preference tuning?▼

Yes, SimPO supports optional SFT regularization via an sft_weight term to preserve capabilities and reduce forgetting, which is especially useful when starting from instruct or chat models.

What distinguishes SimPO from other preference optimization methods for LLM alignment?▼

SimPO distinguishes itself by being reference-free and using β and gamma_beta_ratio to scale reward signals and enforce a target preference margin, avoiding the computational overhead of DPO-style reference models.

When should I not use SimPO for preference alignment?▼

If your dataset lacks chosen/rejected preference pairs or requires a reference model for strict policy optimization, SimPO's reference-free approach may not fit your preference alignment requirements.

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