SyncThink: A Training-Free Strategy to Align Inference Termination with Reasoning Saturation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914238154407936 |
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| author | Li, Gengyang Cai, Wang Gao, Yifeng Wu, Yunfang |
| author_facet | Li, Gengyang Cai, Wang Gao, Yifeng Wu, Yunfang |
| contents | Chain-of-Thought (CoT) prompting improves reasoning but often produces long and redundant traces that substantially increase inference cost. We present SyncThink, a training-free and plug-and-play decoding method that reduces CoT overhead without modifying model weights. We find that answer tokens attend weakly to early reasoning and instead focus on the special token "/think", indicating an information bottleneck. Building on this observation, SyncThink monitors the model's own reasoning-transition signal and terminates reasoning. Experiments on GSM8K, MMLU, GPQA, and BBH across three DeepSeek-R1 distilled models show that SyncThink achieves 62.00 percent average Top-1 accuracy using 656 generated tokens and 28.68 s latency, compared to 61.22 percent, 2141 tokens, and 92.01 s for full CoT decoding. On long-horizon tasks such as GPQA, SyncThink can further yield up to +8.1 absolute accuracy by preventing over-thinking. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_03649 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SyncThink: A Training-Free Strategy to Align Inference Termination with Reasoning Saturation Li, Gengyang Cai, Wang Gao, Yifeng Wu, Yunfang Computation and Language Chain-of-Thought (CoT) prompting improves reasoning but often produces long and redundant traces that substantially increase inference cost. We present SyncThink, a training-free and plug-and-play decoding method that reduces CoT overhead without modifying model weights. We find that answer tokens attend weakly to early reasoning and instead focus on the special token "/think", indicating an information bottleneck. Building on this observation, SyncThink monitors the model's own reasoning-transition signal and terminates reasoning. Experiments on GSM8K, MMLU, GPQA, and BBH across three DeepSeek-R1 distilled models show that SyncThink achieves 62.00 percent average Top-1 accuracy using 656 generated tokens and 28.68 s latency, compared to 61.22 percent, 2141 tokens, and 92.01 s for full CoT decoding. On long-horizon tasks such as GPQA, SyncThink can further yield up to +8.1 absolute accuracy by preventing over-thinking. |
| title | SyncThink: A Training-Free Strategy to Align Inference Termination with Reasoning Saturation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.03649 |