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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.17627 |
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| _version_ | 1866908760926060544 |
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| author | Wei, Zihao Pang, Liang Liu, Jiahao Shi, Wenjie Deng, Jingcheng Xu, Shicheng Duan, Zenghao Sun, Fei Shen, Huawei Cheng, Xueqi |
| author_facet | Wei, Zihao Pang, Liang Liu, Jiahao Shi, Wenjie Deng, Jingcheng Xu, Shicheng Duan, Zenghao Sun, Fei Shen, Huawei Cheng, Xueqi |
| contents | Test-time scaling via explicit reasoning trajectories significantly boosts large language model (LLM) performance but often triggers overthinking. To explore this, we analyze reasoning through two lenses: Reasoning Length Dynamics, which reveals a compensatory trade-off between thinking and answer content length that eventually leads to thinking redundancy, and Reasoning Semantic Dynamics, which identifies semantic convergence and repetitive oscillations. These dynamics uncover an instance-specific Reasoning Completion Point (RCP), beyond which computation continues without further performance gain. Since the RCP varies across instances, we propose a Reasoning Completion Point Detector (RCPD), an inference-time early-exit method that identifies the RCP by monitoring the rank dynamics of termination tokens (e.g., </think>). Across AIME and GPQA benchmarks using Qwen3 and DeepSeek-R1, RCPD reduces token usage by up to 44% while preserving accuracy, offering a principled approach to efficient test-time scaling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17627 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis Wei, Zihao Pang, Liang Liu, Jiahao Shi, Wenjie Deng, Jingcheng Xu, Shicheng Duan, Zenghao Sun, Fei Shen, Huawei Cheng, Xueqi Computation and Language Artificial Intelligence Test-time scaling via explicit reasoning trajectories significantly boosts large language model (LLM) performance but often triggers overthinking. To explore this, we analyze reasoning through two lenses: Reasoning Length Dynamics, which reveals a compensatory trade-off between thinking and answer content length that eventually leads to thinking redundancy, and Reasoning Semantic Dynamics, which identifies semantic convergence and repetitive oscillations. These dynamics uncover an instance-specific Reasoning Completion Point (RCP), beyond which computation continues without further performance gain. Since the RCP varies across instances, we propose a Reasoning Completion Point Detector (RCPD), an inference-time early-exit method that identifies the RCP by monitoring the rank dynamics of termination tokens (e.g., </think>). Across AIME and GPQA benchmarks using Qwen3 and DeepSeek-R1, RCPD reduces token usage by up to 44% while preserving accuracy, offering a principled approach to efficient test-time scaling. |
| title | The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17627 |