@inproceedings{chang-etal-2025-step,
title = "Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models",
author = "Chang, Kaiyan and
Shi, Yonghao and
Wang, Chenglong and
Zhou, Hang and
Hu, Chi and
Liu, Xiaoqian and
Luo, Yingfeng and
Ge, Yuan and
Xiao, Tong and
Zhu, JingBo",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.931/",
pages = "18473--18488",
ISBN = "979-8-89176-332-6",
abstract = "Test-Time Scaling (TTS) is a promising approach to progressively elicit the model{'}s intelligence during inference. Recently, training-based TTS methods, such as continued reinforcement learning (RL), have further surged in popularity, while training-free TTS methods are gradually fading from prominence. However, the additional computation overhead of training amplifies the burden on test-time scaling.In this paper, we focus on training-free TTS methods for reasoning. We first design Conditional Step-level Self-refinement, a fine-grained sequential scaling method guided by process verification. On top of its effectiveness, we further combine it with other classical parallel scaling methods at the step level, to introduce a novel inference paradigm called Hybrid Test-Time Scaling. Extensive experiments on five instruction-tuned LLMs across different scales (3B-14B) and families demonstrate that hybrid strategy incorporating various training-free TTS methods at a fine granularity has considerable potential for expanding the reasoning performance boundaries of LLMs."
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<abstract>Test-Time Scaling (TTS) is a promising approach to progressively elicit the model’s intelligence during inference. Recently, training-based TTS methods, such as continued reinforcement learning (RL), have further surged in popularity, while training-free TTS methods are gradually fading from prominence. However, the additional computation overhead of training amplifies the burden on test-time scaling.In this paper, we focus on training-free TTS methods for reasoning. We first design Conditional Step-level Self-refinement, a fine-grained sequential scaling method guided by process verification. On top of its effectiveness, we further combine it with other classical parallel scaling methods at the step level, to introduce a novel inference paradigm called Hybrid Test-Time Scaling. Extensive experiments on five instruction-tuned LLMs across different scales (3B-14B) and families demonstrate that hybrid strategy incorporating various training-free TTS methods at a fine granularity has considerable potential for expanding the reasoning performance boundaries of LLMs.</abstract>
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%0 Conference Proceedings
%T Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models
%A Chang, Kaiyan
%A Shi, Yonghao
%A Wang, Chenglong
%A Zhou, Hang
%A Hu, Chi
%A Liu, Xiaoqian
%A Luo, Yingfeng
%A Ge, Yuan
%A Xiao, Tong
%A Zhu, JingBo
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F chang-etal-2025-step
%X Test-Time Scaling (TTS) is a promising approach to progressively elicit the model’s intelligence during inference. Recently, training-based TTS methods, such as continued reinforcement learning (RL), have further surged in popularity, while training-free TTS methods are gradually fading from prominence. However, the additional computation overhead of training amplifies the burden on test-time scaling.In this paper, we focus on training-free TTS methods for reasoning. We first design Conditional Step-level Self-refinement, a fine-grained sequential scaling method guided by process verification. On top of its effectiveness, we further combine it with other classical parallel scaling methods at the step level, to introduce a novel inference paradigm called Hybrid Test-Time Scaling. Extensive experiments on five instruction-tuned LLMs across different scales (3B-14B) and families demonstrate that hybrid strategy incorporating various training-free TTS methods at a fine granularity has considerable potential for expanding the reasoning performance boundaries of LLMs.
%U https://aclanthology.org/2025.emnlp-main.931/
%P 18473-18488
Markdown (Informal)
[Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models](https://aclanthology.org/2025.emnlp-main.931/) (Chang et al., EMNLP 2025)
ACL
- Kaiyan Chang, Yonghao Shi, Chenglong Wang, Hang Zhou, Chi Hu, Xiaoqian Liu, Yingfeng Luo, Yuan Ge, Tong Xiao, and JingBo Zhu. 2025. Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 18473–18488, Suzhou, China. Association for Computational Linguistics.