Majority Voting for Code Generation

Tim Launer; Jonas Hübotter; Marco Bagatella; Ido Hakimi; Andreas Krause

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arXiv · 2604.15618

Majority Voting for Code Generation

Tim Launer·Jonas Hübotter·Marco Bagatella·Ido Hakimi·Andreas Krause

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Abstract

We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies a representative solution from multiple generations using their runtime execution signatures on test inputs. We find that FMV is an effective test-time inference strategy, substantially boosting performance on LiveCodeBench without a large compute overhead. Furthermore, we extend the utility of functional consensus and apply it as an aggregation strategy for label-free Test-Time Reinforcement Learning. We demonstrate that this increases pass@1 on holdout tasks, but find no evidence of self-improvement beyond the base model's performance ceiling.

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Tim Launer, Jonas Hübotter, Marco Bagatella, Ido Hakimi, Andreas Krause. 2026-04-17. Majority Voting for Code Generation. https://arxiv.org/abs/2604.15618

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