# SBSC: Step-By-Step Coding for Improving Mathematical Olympiad Performance

arXiv: https://arxiv.org/abs/2502.16666 · HTML: https://mlsanity.com/paper/2502.16666/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Mathematical reasoning, one of 17 directions in LLM reasoning.
- First posted on arXiv 23 Feb 2025.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Built on by (most cited first, 4 of 4)

- [Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs](https://mlsanity.com/paper/2509.24107/) (2025)
- [A Survey of Deep Learning for Geometry Problem Solving](https://mlsanity.com/paper/2507.11936/) (2025)
- [Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models](https://mlsanity.com/paper/2605.19663/) (2026)
- [Reasoning, Code, or Both? How Large Language Models Handle Variations in Math Questions](https://mlsanity.com/paper/2605.26414/) (2026)

## Abstract (arXiv)

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. At each step/turn, by leveraging the code execution outputs and programs of previous steps, the model generates the next sub-task and the corresponding program to solve it. This way, SBSC, sequentially navigates to reach the final answer. 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.