Learning to Think from Multiple Thinkers
📰 ArXiv cs.AI
arXiv:2604.24737v1 Announce Type: cross Abstract: We study learning with Chain-of-Thought (CoT) supervision from multiple thinkers, all of whom provide correct but possibly systematically different solutions, e.g., step-by-step solutions to math problems written by different thinkers, or step-by-step execution traces of different programs solving the same problem. We consider classes that are computationally easy to learn using CoT supervision from a single thinker, but hard to learn with only e
DeepCamp AI