Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning

Algorithmic reasoning---the ability to perform step-by-step logical inference---has become a core benchmark for evaluating reasoning in graph neural networks (GNNs) and large language models (LLMs). Ideally, one would like to design a single model capable of performing well on multiple algorithmic reasoning tasks simultaneously. However, this is challenging when the execution steps of algorithms differ from one another, causing negative interference when they are trained together.