Weak-to-Strong Reasoning
Free while signed in. Answers cite the passages they came from.
First page
The curator’s take
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.
Every Monday
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack