Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing
Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-trajectory signals and lack principled mechanisms to exploit global dynamics across parallel branches. We introduce 2D probing, an interface that exposes the width-depth dynamics of parallel thinking by periodically eliciting intermediate answers from all branches. Our analysis reveals three key insights: non-monotonic scaling across width-depth allocations, heterogeneous reasoning branch lengths, and early stabilization of global consensus. Guided by these insights, we introduce Parallel-Probe, a training-free controller designed to optimize online parallel thinking. Parallel-Probe employs consensus-based early stopping to regulate reasoning depth and deviation-based branch pruning to dynamically adjust width. Extensive experiments across three benchmarks and multiple models demonstrate that Parallel-Probe establishes a superior Pareto frontier for test-time scaling. Compared to standard majority voting, it reduces sequential tokens by up to 35.8% and total token cost by over 25.8% while maintaining competitive accuracy.
6982bc249084cb4f0ecb56da
Tong Zheng
6982bc249084cb4f0ecb56db
Chengsong Huang
claimed_verified
2026-02-04T12:28:28.107Z
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Chengsong Huang
ChengsongHuang
user
ChengsongHuang
6982bc249084cb4f0ecb56dc
Runpeng Dai
claimed_verified
2026-02-04T12:28:30.438Z
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Runpeng Dai
Leo-Dai
user
Leo-Dai
6982bc249084cb4f0ecb56dd
Yun He
6982bc249084cb4f0ecb56de
Rui Liu
6982bc249084cb4f0ecb56df
Xin Ni
6982bc249084cb4f0ecb56e0
Huiwen Bao
6982bc249084cb4f0ecb56e1
Kaishen Wang
6982bc249084cb4f0ecb56e2
Hongtu Zhu
6982bc249084cb4f0ecb56e3
Jiaxin Huang
6982bc249084cb4f0ecb56e4
Furong Huang
6982bc249084cb4f0ecb56e5
Heng Huang
Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing
Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-trajectory signals and lack principled mechanisms to exploit global dynamics across parallel branches. We introduce 2D probing, an interface that exposes the width-depth dynamics of parallel thinking by periodically eliciting intermediate answers from all branches. Our analysis reveals three key insights: non-monotonic scaling across width-depth allocations, heterogeneous reasoning branch lengths, and early stabilization of global consensus. Guided by these insights, we introduce Parallel-Probe, a training-free controller designed to optimize online parallel thinking. Parallel-Probe employs consensus-based early stopping to regulate reasoning depth and deviation-based branch pruning to dynamically adjust width. Extensive experiments across three benchmarks and multiple models demonstrate that Parallel-Probe establishes a superior Pareto frontier for test-time scaling. Compared to standard majority voting, it reduces sequential tokens by up to 35.8% and total token cost by over 25.8% while maintaining competitive accuracy.
Parallel-Probe is a training-free controller that optimizes parallel thinking by using consensus-based early stopping and deviation-based branch pruning to reduce computational costs while maintaining accuracy.
parallel thinking width-depth dynamics intermediate answers consensus-based early stopping deviation-based branch pruning Pareto frontier test-time scaling majority voting