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This research reframes pause tokens not merely as inference-time tools but as a specific training-dynamics intervention that optimizes the retention-adaptation trade-off in large language models. By masking loss at reasoning-step boundaries, the proposed Masked Boundary Pause (MBP) technique significantly boosts performance on math and code tasks across Qwen and Llama architectures while preserving general capabilities. The findings offer a novel perspective on how fine-tuning strategies can be engineered to enhance model reasoning without sacrificing prior knowledge.
Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective
arXiv:2609.04489v1 Announce Type: new 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 4x 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 this mode-preserving strategy extend gains 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: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 4x 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 this mode-preserving strategy extend gains 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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.04489 [cs.CL]
(or arXiv:2609.04489v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.04489 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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