In this episode, we discuss Learning Task Decomposition to Assist Humans in Competitive Programming by Jiaxin Wen, Ruiqi Zhong, Pei Ke, Zhihong Shao, Hongning Wang, Minlie Huang. The paper presents a method to enhance human understanding and repair of language model (LM)-generated solutions by automatically breaking down complex solutions into simpler subtasks. They introduce a novel objective called assistive value (AssistV) to measure how easily humans can repair these subtasks and validate their method through a dataset of human repair experiences. The approach significantly improves the problem-solving ability and speed of non-experts in competitive programming, allowing them to solve more problems and match the performance of unassisted experts.
AI Breakdown