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
影响 Introduces a new learning paradigm that could improve model generalization and robustness by leveraging diverse reasoning paths.
排序理由 Academic paper on a novel machine learning technique.
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
