@inproceedings{nan-etal-2026-rhombus,
title = "Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning",
author = "Nan, Ziyuan and
Yi, Qi and
Huang, Di and
Wu, Yutong and
Huang, Guanhua and
Gong, Xue and
Li, Kejiao and
Jiang, Yuhao and
Zhang, Chenchen and
Xu, Zenan and
Hu, Xing and
Zhou, Bo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1956/",
pages = "39258--39270",
ISBN = "979-8-89176-395-1",
abstract = "Parallel thinking offers a promising avenue for scaling test-time compute in Large Language Models (LLMs), enabling them to explore diverse solution paths simultaneously before aggregating them into a final answer. However, coordinating the exploration and aggregation stages remains challenging, as simple aggregation techniques often incur information loss, failing to preserve the subtle, decision-relevant signals generated during exploration. To overcome this, we propose Rhombus, a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning. Rhombus employs multiple parallel Proposers to generate compact, decision-focused reasoning cues and a central Synthesizer to integrate them into final predictions, utilizing co-training under a shared task reward to align their interaction. Across challenging mathematical reasoning benchmarks, Rhombus improves accuracy by 6.0{\%} over long chain-of-thought baselines while reducing wall-clock latency by 39.4{\%} under matched token budgets. Our work demonstrates that explicit communication optimization is essential for realizing the accuracy and efficiency gains of parallel reasoning."
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<abstract>Parallel thinking offers a promising avenue for scaling test-time compute in Large Language Models (LLMs), enabling them to explore diverse solution paths simultaneously before aggregating them into a final answer. However, coordinating the exploration and aggregation stages remains challenging, as simple aggregation techniques often incur information loss, failing to preserve the subtle, decision-relevant signals generated during exploration. To overcome this, we propose Rhombus, a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning. Rhombus employs multiple parallel Proposers to generate compact, decision-focused reasoning cues and a central Synthesizer to integrate them into final predictions, utilizing co-training under a shared task reward to align their interaction. Across challenging mathematical reasoning benchmarks, Rhombus improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets. Our work demonstrates that explicit communication optimization is essential for realizing the accuracy and efficiency gains of parallel reasoning.</abstract>
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%0 Conference Proceedings
%T Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning
%A Nan, Ziyuan
%A Yi, Qi
%A Huang, Di
%A Wu, Yutong
%A Huang, Guanhua
%A Gong, Xue
%A Li, Kejiao
%A Jiang, Yuhao
%A Zhang, Chenchen
%A Xu, Zenan
%A Hu, Xing
%A Zhou, Bo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F nan-etal-2026-rhombus
%X Parallel thinking offers a promising avenue for scaling test-time compute in Large Language Models (LLMs), enabling them to explore diverse solution paths simultaneously before aggregating them into a final answer. However, coordinating the exploration and aggregation stages remains challenging, as simple aggregation techniques often incur information loss, failing to preserve the subtle, decision-relevant signals generated during exploration. To overcome this, we propose Rhombus, a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning. Rhombus employs multiple parallel Proposers to generate compact, decision-focused reasoning cues and a central Synthesizer to integrate them into final predictions, utilizing co-training under a shared task reward to align their interaction. Across challenging mathematical reasoning benchmarks, Rhombus improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets. Our work demonstrates that explicit communication optimization is essential for realizing the accuracy and efficiency gains of parallel reasoning.
%U https://aclanthology.org/2026.findings-acl.1956/
%P 39258-39270
Markdown (Informal)
[Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning](https://aclanthology.org/2026.findings-acl.1956/) (Nan et al., Findings 2026)
ACL
- Ziyuan Nan, Qi Yi, Di Huang, Yutong Wu, Guanhua Huang, Xue Gong, Kejiao Li, Yuhao Jiang, Chenchen Zhang, Zenan Xu, Xing Hu, and Bo Zhou. 2026. Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39258–39270, San Diego, California, United States. Association for Computational Linguistics.