Weak-to-Strong Reasoning
First page
Paper summary
demonstrates the use of weak supervision to elicit strong reasoning capabilities in LLMs without relying on human annotations or advanced models; reports that strong models can automatically refine their training data without explicitly being trained to do so; enables expanding a model's learning scope and scaling performance on reasoning.