arXiv AI September 7, 2026 By Jaehyeon Kim, Suhwan Kim, Nakyung Lee, Yeongoon Kim, Jimin Seo, Giho Lee, Jungwoo Lee

Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective

The paper investigates how pause tokens affect fine‑tuning dynamics rather than just inference performance. Experiments on synthetic continual‑learning and math‑reasoning tasks show that masked pause tokens (MBP) reduce overwriting of learned modes and compress downstream‑step information. Across 1B‑8B Qwen and Llama models, MBP consistently improves reasoning scores while preserving language understanding, and the approach also benefits GRPO.

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arXiv AI

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PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning

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By Lingyu Jiang, Zirui Li, Lingyu Xu, Shuo Xing, Peiran Li, Tsubasa Takahashi, Dengzhe Hou, Zhengzhong Tu, Kazunori Yamada, Fangzhou Lin

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arXiv Computation and Language

Aug 24

Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

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By Simeng Zhang, Yilong Chen, Wenyuan Zhang, Zhenyu Zhang, Yao Chen, Junyuan Shang, Tingwen Liu

llms nlp benchmarks

arXiv AI

Sep 30

Beyond Compression: Diagnosing How Post-Training Changes Mathematical Reasoning

arXiv:2609.37066v1 Announce Type: cross Abstract: Post-training is central to mathematical reasoning in modern large language models (LLMs), but endpoint pass@1 alone underidentifies what has changed...

By Hongyang Li, Yiming Zhu, Xiao Li, Caesar Wu, Said Mammar, Pascal Bouvry

llms fine-tuning efficiency

arXiv AI

Sep 1

Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training

The paper investigates how different forms of compressed chain‑of‑thought (CoT) reasoning—Explicit, Composed, and Implicit—affect large language model (LLM) performance after supervised fine‑tuning (SFT). Using a synthetic compositional reasoning task, the authors show that coarser CoT requires more SFT data, that Composed and Implicit CoT benefit more from data scaling (with Composed also benefiting from repetition), and that reinforcement learning with verifiable rewards (RLVR) can decompose compressed steps learned during SFT. Additionally, unidirectional CoT ordering improves generalization on longer sequential tasks.

By Kohsei Matsutani, Gouki Minegishi, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo

llms reinforcement-learning fine-tuning

arXiv AI

Jul 2

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models

arXiv:2512. 18934v2 Announce Type: replace-cross Abstract: Catastrophic forgetting poses a fundamental challenge in continual learning, particularly when models are quantized for deployment efficiency.

By Michael S. Zhang, Rishi A. Ruia, Arnav Kewalram, Saathvik Dharmapuram, Utkarsh Sharma, Kevin Zhu

llms efficiency

arXiv Machine Learning

2d ago

Learning, Fast and Slow: Towards LLMs That Adapt Continually

arXiv:2605.12484v3 Announce Type: replace Abstract: Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating model parameters force...

By Rishabh Tiwari, Kusha Sareen, Lakshya A Agrawal, Joseph E. Gonzalez, Matei Zaharia, Kurt Keutzer, Inderjit S Dhillon, Rishabh Agarwal, Devvrit Khatri

llms reinforcement-learning

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PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning

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