Think in Parallel, Answer as One: Logit Averaging for Open-Ended Reasoning
Haonan Wang, Chao Du, Kenji Kawaguchi, Tianyu Pang
Abstract
Majority voting has proven effective for close-ended question answering by aggregating parallel reasoning traces. However, it is not directly applicable to open-ended reasoning, where "majority" is undefined. We introduce THINKMERGE, a training-free, plug-and-play decoding strategy that runs K parallel reasoning traces and averages their next-token logits at synchronization points to produce a single coherent output. THINKMERGE integrates seamlessly with vLLM/SGLang and remains compatible with standard decoding techniques such as Top-p/Top-k. Empirically, it matches or surpasses majority voting on AIME and GPQA, while delivering consistent gains on open-ended coding tasks: on LiveCodeBench (hard), pass@1 improves by +8.28% for DeepCoder-14B-Preview and +7.58% for Qwen3-8B. These results demonstrate that parallel test-time scaling can benefit open-ended reasoning without relying on voting over complete outputs.
BibTeX
@inproceedings{
wang2026think,
title={Think in Parallel, Answer as One: Logit Averaging for Open-Ended Reasoning},
author={Haonan Wang and Chao Du and Kenji Kawaguchi and Tianyu Pang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=hvit36Dyzl}
}