Not All LLM Reasoners Are Created Equal
Authors: name: Arian Hosseini, name: Alessandro Sordoni, name: Daniel Toyama, name: Aaron Courville, name: Rishabh Agarwal

We study the depth of grade-school math (GSM) problem-solving capabilities of
LLMs. To this end, we evaluate their performance on pairs of existing math word
problems together so that the answer to the second problem depends on correctly
answering the first problem. Our findings reveal a significant reasoning gap in
most LLMs, that is performance difference between solving the compositional
pairs and solving each question independently. This gap is more pronounced in
smaller, more cost-efficient, and math-specialized models. Moreover,
instruction-tuning recipes and code generation have varying effects across LLM
sizes, while finetuning on GSM can lead to task overfitting. Our analysis
indicates that large reasoning gaps are not because of test-set leakage, but
due to distraction from additional context and poor second-hop reasoning.
Overall, LLMs exhibit systematic differences in their reasoning abilities,
despite what their performance on standard benchmarks indicates.