SyncThink: A Training-Free Strategy to Align Inference Termination with Reasoning Saturation

• URL: http://arxiv.org/abs/2601.03649v1
• Date: Wed, 07 Jan 2026 07:00:15 GMT
• Title: SyncThink: A Training-Free Strategy to Align Inference Termination with Reasoning Saturation
• Authors: Gengyang Li, Wang Cai, Yifeng Gao, Yunfang Wu,
• Abstract summary: We present SyncThink, a training-free and plug-and-play decoding method that reduces Chain-of-Thought overhead without modifying model weights.<n>We find that answer tokens attend weakly to early reasoning and instead focus on the special token "/think", indicating an information bottleneck.<n>Experiments on GSM8K, MMLU, GPQA, and BBH across three DeepSeek-R1 distilled models show that SyncThink achieves 62.00 percent average Top-1 accuracy.
• Score: 11.021989271617835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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