Entropy-Guided Loop – How to make small models reason

The Entropy-Guided Loop project creates self-correcting AI models by tracking token-level uncertainty during generation. When the model detects high uncertainty in its response, it automatically triggers a refinement pass to improve accuracy. This approach reduces costs by 2.75x compared to dedicated reasoning models while maintaining similar quality, working by leveraging normally discarded probability distributions to identify and fix potential errors.