simpo-training

Reference-free preference optimization allows you to align LLMs directly from human feedback using chosen and rejected pairs, eliminating the need for a separate reward or reference policy. SimPO applies this to guide model updates efficiently.

Reference-free preference optimization is a training mechanism that updates model preferences directly from comparative feedback. It bypasses the need for a separate reward model by using loss functions applied to chosen and rejected preference datasets.

Yes, SimPO applies to a range of base models and datasets for alignment tasks including instruction following and reasoning problems. It uses configurable hyperparameters and data pipelines for end-to-end preference learning.

You configure hyperparameters and loss types within the training loop to manage preference optimization. The system supports optional SFT regularization alongside configurable settings to satisfy production-ready alignment requirements.

No, reference-free preference optimization does not require a separate reward model. It uses chosen and rejected preferences directly from your datasets to guide model updates, enabling scalable alignment experiments without a reference policy.

Launch a minimal training run using default settings to start learning preferences from your data. This quick start approach allows you to test the alignment pipeline before applying custom hyperparameters or optional SFT regularization.