SBSC: Step-by-Step Coding for Improving Mathematical Olympiad Performance
Keywords: AI Math Reasoning, LLM Math Reasoning, Olympiad-Math
Abstract: We propose Step-by-Step Coding (SBSC): a multi-turn math reasoning framework
that enables Large Language Models (LLMs) to generate sequence of programs
for solving Olympiad level math problems. After each turn/step, by leveraging
the code execution outputs and programs of previous steps, the model generates
the next sub-task and the corresponding program to complete it. SBSC allows
more granular, flexible and precise approach to problem-solving compared to
existing methods. Extensive experiments highlight the effectiveness of SBSC in
tackling competition and Olympiad-level math problems. For Claude-3.5-Sonnet,
we observe SBSC (greedy decoding) surpasses existing state-of-the-art (SOTA)
program generation based reasoning strategies by absolute 10.7% on AMC12, 8%
on AIME and 12.6% on MathOdyssey. Given SBSC is multi-turn in nature, we also
benchmark SBSC’s greedy decoding against self-consistency decoding results of
existing SOTA math reasoning strategies and observe performance gain by absolute
6.2% on AMC, 6.7% on AIME and 7.4% on MathOdyssey.
Concurrent Submissions: ICLR
Submission Number: 26
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