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simpo-training

Automates reference-free optimization of user preferences for LLM behavior alignment.

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

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 provides a reference-free approach to aligning large language models by learning from preference data without requiring a separate reference model.

Core Features & Use Cases

  • Reference-free alignment: trains with chosen/rejected preferences without a reference model.
  • Configurable optimization: supports beta, gamma_beta_ratio, loss_type, label_smoothing, and optional SFT regularization.
  • Workflow-ready: includes scripts and configs to run training on standard models with a straightforward path from data to deployment.
  • Use cases: improving alignment for instruction-following tasks where labeled comparative data is scarce.

Quick Start

Run the SimPO training pipeline with the provided configs to align a base model 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 an LLM with preference data without a reference model?▼

Reference-free preference optimization aligns LLMs using chosen and rejected pairs without a separate reference model. SimPO automates this by configuring beta, gamma_beta_ratio, and loss_type to guide behavior, reducing training complexity.

What is the difference between SimPO and standard reinforcement learning for LLM alignment?▼

SimPO is a reference-free preference optimization method, unlike standard RL approaches that require a separate reference model. It simplifies alignment by directly optimizing preferences using configurable parameters like beta and label_smoothing.

Can I use transformers and torch datasets for reference-free preference optimization?▼

Yes, reference-free preference optimization works with transformers, torch, datasets, and accelerate. The Skill provides scripts and configs to run end-to-end training on standard open-weight models using these dependencies.

How do I configure beta and label_smoothing for SimPO training?▼

SimPO training is configured by adjusting beta, gamma_beta_ratio, loss_type, label_smoothing, and optional SFT regularization. These parameters control the optimization process for aligning LLM behavior with preference data.

When should I use reference-free preference optimization for instruction-following tasks?▼

Reference-free preference optimization is ideal for instruction-following tasks where labeled comparative data is scarce. It provides a faster, simpler alignment path without the overhead of loading and managing a reference model.

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