CALM Before the STORM: Unlocking Native Reasoning for Optimization
  Modeling
Authors: name: Zhengyang Tang, name: Zihan Ye, name: Chenyu Huang, name: Xuhan Huang, name: Chengpeng Li, name: Sihang Li, name: Guanhua Chen, name: Ming Yan, name: Zizhuo Wang, name: Hongyuan Zha, name: Dayiheng Liu, name: Benyou Wang

Large Reasoning Models (LRMs) have demonstrated strong capabilities in
complex multi-step reasoning, opening new opportunities for automating
optimization modeling. However, existing domain adaptation methods, originally
designed for earlier instruction-tuned models, often fail to exploit the
advanced reasoning patterns of modern LRMs -- In particular, we show that
direct fine-tuning on traditional non-reflective datasets leads to
limited gains. To fully leverage LRMs' inherent reasoning abilities, we propose
CALM (Corrective Adaptation with Lightweight Modification), a
framework that progressively refines LRMs within their native reasoning modes
for optimization modeling tasks. In CALM, an expert intervener identifies
reasoning flaws and provides concise corrective hints, which the LRM
incorporates to produce improved reasoning trajectories. These interventions
modify fewer than 2.6\% of generated tokens, but generate high-quality data for
soft adaptation through supervised fine-tuning. The adapted model is then
further improved through reinforcement learning. Building on CALM, we develop
STORM (Smart Thinking Optimization Reasoning Model), a
4B-parameter LRM that achieves a new state-of-the-art average accuracy of
68.9\% across five popular optimization modeling benchmarks, matching the
performance of a 671B LRM. These results demonstrate that dynamic, hint-based
data synthesis both preserves and amplifies the native reasoning patterns of
modern LRMs, offering a more effective and scalable path towards expert-level
performance on challenging optimization modeling tasks.