Chain of Thought in Order: Discovering Learning-Friendly Orders for Arithmetic

Abstract: The chain of thought is fundamental in Transformers, which is to perform step-by-step reasoning. Besides what intermediate steps work, the order of these steps critically affects the difficulty of the reasoning. This study addresses a novel task of unraveling chain of thought - reordering decoder input tokens to a learning-friendly sequence for Transformers to learn arithmetic tasks. The proposed pipeline first trains a Transformer on a mixture of target sequences arranged in different orders and then identifies benign orders as those with fast loss drops in the early stage. As the search space grows factorially with sequence length, we propose a two-stage hierarchical approach for inter- and intra-block reordering. Experiments on four order-sensitive arithmetic tasks show that our method identifies a learning-friendly order out of a few billion candidates. Notably, on the multiplication task, it recovered the reverse-digit order reported in prior studies.