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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17421 |
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| _version_ | 1866908785809817600 |
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| author | Hwang, Jaehui Han, Dongyoon Yun, Sangdoo Heo, Byeongho |
| author_facet | Hwang, Jaehui Han, Dongyoon Yun, Sangdoo Heo, Byeongho |
| contents | The emergence of discourse-like tokens such as "wait" and "therefore" in large language models (LLMs) has offered a unique window into their reasoning processes. However, systematic analyses of how such signals vary across training strategies and model scales remain lacking. In this paper, we analyze token-level signals through token probabilities across various models. We find that specific tokens strongly correlate with reasoning correctness, varying with training strategies while remaining stable across model scales. A closer look at the "wait" token in relation to answer probability demonstrates that models fine-tuned on small-scale datasets acquire reasoning ability through such signals but exploit them only partially. This work provides a systematic lens to observe and understand the dynamics of LLM reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17421 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Oops, Wait: Token-Level Signals as a Lens into LLM Reasoning Hwang, Jaehui Han, Dongyoon Yun, Sangdoo Heo, Byeongho Computation and Language The emergence of discourse-like tokens such as "wait" and "therefore" in large language models (LLMs) has offered a unique window into their reasoning processes. However, systematic analyses of how such signals vary across training strategies and model scales remain lacking. In this paper, we analyze token-level signals through token probabilities across various models. We find that specific tokens strongly correlate with reasoning correctness, varying with training strategies while remaining stable across model scales. A closer look at the "wait" token in relation to answer probability demonstrates that models fine-tuned on small-scale datasets acquire reasoning ability through such signals but exploit them only partially. This work provides a systematic lens to observe and understand the dynamics of LLM reasoning. |
| title | Oops, Wait: Token-Level Signals as a Lens into LLM Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.17421 |