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QuasiMoTTo achieves up to 47% fewer samples while maintaining accuracy, challenging the conventional wisdom that independent sampling is necessary for effective parallelization.
Forget vague AGI claims – this cognitive taxonomy provides a concrete, measurable framework to map AI capabilities against human cognitive abilities.
CORE enables language models to rapidly enhance reasoning capabilities using minimal training data and rollouts, outperforming traditional methods.
Language models can now learn to forget strategically, achieving 2-3x memory efficiency without sacrificing reasoning accuracy.
