simpo-training

Reference-free preference optimization aligns large language models with human preferences without requiring a ground-truth reference model. It improves instruction-following and safety alignment directly by optimizing model responses based on configurable preference data.

You can apply SimPO for LLM alignment by providing a minimal YAML configuration and running a single training pass. The workflow supports base, instruct, and chat models with tunable hyperparameters like beta, gamma, learning rate, and SFT weight.

No, you do not need a reference model for preference optimization using SimPO. It is a reference-free approach that optimizes large language model alignment directly from preference data without depending on a separate baseline model.

Yes, you can customize hyperparameters for SimPO fine-tuning across base, instruct, and chat models. The workflow allows you to tune beta, gamma, learning rate, and SFT weight to achieve reproducible alignment experiments.

Running reference-free LLM alignment workflows requires installing torch, transformers, datasets, trl, and accelerate. These dependencies support the SimPO training pipeline, dataset mixing, and model fine-tuning operations.

Choose SimPO over standard RLHF for efficient, reference-free LLM alignment. It eliminates the need for a ground-truth reference model during preference optimization, reducing computational overhead while maintaining instruction-following and safety improvements.