@inproceedings{kim-etal-2026-towards,
title = "Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective",
author = "Kim, Jaehyeon and
Kim, Suhwan and
Lee, Nakyung and
Kim, Yeongoon and
Seo, Jimin and
Lee, Giho and
Lee, Jungwoo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1710/",
pages = "38957--38980",
ISBN = "979-8-89176-459-0",
abstract = "Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work explains these gains through computational expressivity. However, there is relatively little investigation into the training dynamics of pause tokens. We explore how pause tokens reshape the training dynamics of fine-tuning. Two controlled pilots expose distinct asymmetries. On a synthetic continual-learning task, masked pauses overwrite a previously-learned distribution roughly 4{\texttimes} less at matched final adaptation (H1, $\textbf{mode retention}$); on a synthetic math-reasoning probe, the boundary-adjacent token comes to encode substantially more downstream-step information (H2, $\textbf{non-myopic compression}$). We formalize a training rule consistent with both {---} Masked Boundary Pause (MBP), pause tokens placed at reasoning-step boundaries with their loss masked. Across 1B-8B Qwen and Llama models, MBP consistently improves reasoning, achieving gains of up to 6 points on math and 2.5 points on code, while preserving general language understanding abilities. We further demonstrate that the gains from this mode-preserving strategy extend to GRPO. These results recast pause tokens as a training-dynamics intervention on the retention{--}adaptation trade-off, rather than merely an inference-time computation device."
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<abstract>Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work explains these gains through computational expressivity. However, there is relatively little investigation into the training dynamics of pause tokens. We explore how pause tokens reshape the training dynamics of fine-tuning. Two controlled pilots expose distinct asymmetries. On a synthetic continual-learning task, masked pauses overwrite a previously-learned distribution roughly 4× less at matched final adaptation (H1, mode retention); on a synthetic math-reasoning probe, the boundary-adjacent token comes to encode substantially more downstream-step information (H2, non-myopic compression). We formalize a training rule consistent with both — Masked Boundary Pause (MBP), pause tokens placed at reasoning-step boundaries with their loss masked. Across 1B-8B Qwen and Llama models, MBP consistently improves reasoning, achieving gains of up to 6 points on math and 2.5 points on code, while preserving general language understanding abilities. We further demonstrate that the gains from this mode-preserving strategy extend to GRPO. These results recast pause tokens as a training-dynamics intervention on the retention–adaptation trade-off, rather than merely an inference-time computation device.</abstract>
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%0 Conference Proceedings
%T Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective
%A Kim, Jaehyeon
%A Kim, Suhwan
%A Lee, Nakyung
%A Kim, Yeongoon
%A Seo, Jimin
%A Lee, Giho
%A Lee, Jungwoo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-etal-2026-towards
%X Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work explains these gains through computational expressivity. However, there is relatively little investigation into the training dynamics of pause tokens. We explore how pause tokens reshape the training dynamics of fine-tuning. Two controlled pilots expose distinct asymmetries. On a synthetic continual-learning task, masked pauses overwrite a previously-learned distribution roughly 4× less at matched final adaptation (H1, mode retention); on a synthetic math-reasoning probe, the boundary-adjacent token comes to encode substantially more downstream-step information (H2, non-myopic compression). We formalize a training rule consistent with both — Masked Boundary Pause (MBP), pause tokens placed at reasoning-step boundaries with their loss masked. Across 1B-8B Qwen and Llama models, MBP consistently improves reasoning, achieving gains of up to 6 points on math and 2.5 points on code, while preserving general language understanding abilities. We further demonstrate that the gains from this mode-preserving strategy extend to GRPO. These results recast pause tokens as a training-dynamics intervention on the retention–adaptation trade-off, rather than merely an inference-time computation device.
%U https://aclanthology.org/2026.emnlp-main.1710/
%P 38957-38980
Markdown (Informal)
[Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective](https://aclanthology.org/2026.emnlp-main.1710/) (Kim et al., EMNLP 2026)
ACL