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Context Language Models are introduced, language models that natively manage their own context and can be steered with natural-language instructions evolved through a standard skill-optimization loop, improving held-out accuracy by up to 35.9 points on a context-management task while reducing compute.
NGU, a simple adaptive sampling method that keeps generating samples for a problem until one is correct, is introduced, a simple adaptive sampling method that naturally uses fewer samples to filter out easy problems and allocates more compute to solving harder problems.
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