A new research paper explores the challenges and potential of learning from multiple 'thinkers' that provide distinct, yet correct, step-by-step solutions. The study indicates that while learning can be difficult with CoT supervision from a few thinkers in passive settings, an efficient active learning algorithm can overcome this. This algorithm requires minimal CoT data per thinker, a moderate number of thinkers, and sufficient passive end-result data to achieve target accuracy. AI
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IMPACT Introduces a new learning paradigm that could improve model generalization and robustness by leveraging diverse reasoning paths.
RANK_REASON Academic paper on a novel machine learning technique.