Z1: Efficient Test-time Scaling with Code
Large Language Models (LLMs) can achieve enhanced complex problem-solving
through test-time computing scaling, yet this often entails longer contexts and
numerous reasoning token costs. In this paper, we propose an efficient
test-time scaling method that trains LLMs on code-related reasoning
trajectories, facilitating their reduction of excess thinking tokens while
maintaining performance. First, we create Z1-Code-Reasoning-107K, a curated
dataset of simple and complex coding problems paired with their short and long
solution trajectories. Second, we present a novel Shifted Thinking Window to
mitigate overthinking overhead by removing context-delimiting tags (e.g.,
<think>. . . </think>) and capping reasoning tokens. Trained with long and
short trajectory data and equipped with Shifted Thinking Window, our model,
Z1-7B, demonstrates the ability to adjust its reasoning level as the complexity
of problems and exhibits efficient test-time scaling across different reasoning
tasks that matches R1-Distill-Qwen-7B performance with about 30% of its average
thinking tokens. Notably, fine-tuned with only code trajectories, Z1-7B
demonstrates generalization to broader reasoning tasks (47.5% on GPQA Diamond).
Our analysis of efficient reasoning elicitation also provides valuable insights
for future research.
67eca0c4cfce948cbbbcff9a
Zhaojian Yu
claimed_verified
2025-04-02T08:22:38.129Z
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Zhaojian Yu
user
zjy2001
67eca0c4cfce948cbbbcff9b
Yinghao Wu
admin_assigned
2025-04-02T09:50:27.577Z
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Yinghao Wu
user
yh1567
67eca0c4cfce948cbbbcff9c
Yilun Zhao
claimed_verified
2025-04-02T08:22:34.689Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62f662bcc58915315c4eccea/zOAQLONfMP88zr70sxHK-.jpeg
Yilun Zhao
user
yilunzhao
67eca0c4cfce948cbbbcff9d
Arman Cohan
admin_assigned
2025-04-02T09:50:33.530Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1599840760465-noauth.png
Arman Cohan
user
armanc
67eca0c4cfce948cbbbcff9e
Xiao-Ping Zhang
Z1: Efficient Test-time Scaling with Code
Large Language Models (LLMs) can achieve enhanced complex problem-solving
through test-time computing scaling, yet this often entails longer contexts and
numerous reasoning token costs. In this paper, we propose an efficient
test-time scaling method that trains LLMs on code-related reasoning
trajectories, facilitating their reduction of excess thinking tokens while
maintaining performance. First, we create Z1-Code-Reasoning-107K, a curated
dataset of simple and complex coding problems paired with their short and long
solution trajectories. Second, we present a novel Shifted Thinking Window to
mitigate overthinking overhead by removing context-delimiting tags (e.g.,
<think>. . . </think>) and capping reasoning tokens. Trained with long and
short trajectory data and equipped with Shifted Thinking Window, our model,
Z1-7B, demonstrates the ability to adjust its reasoning level as the complexity
of problems and exhibits efficient test-time scaling across different reasoning
tasks that matches R1-Distill-Qwen-7B performance with about 30% of its average
thinking tokens. Notably, fine-tuned with only code trajectories, Z1-7B
demonstrates generalization to broader reasoning tasks (47.5% on GPQA Diamond).
Our analysis of efficient reasoning elicitation also provides valuable insights
for future research.
A novel Shifted Thinking Window method trains LLMs on code-related reasoning trajectories to reduce excessive thinking tokens while maintaining performance and efficient test-time scaling.
LLMs test-time computing scaling reasoning trajectories Z1-Code-Reasoning-107K Shifted Thinking Window context-delimiting tags reasoning tokens Z1-7B efficient test-time scaling R1-Distill-Qwen-7B fine-tuning GPQA Diamond efficient reasoning elicitation