13 papers
R-CoT: A Reasoning-Layer Watermark via Redundant Chain-of-Thought in Large Language Models
Ziming Zhang, Li Li, Guorui Feng +2
Large language models (LLMs) are widely deployed in multiple scenarios due to reasoning capabilities. In order to prevent the models from being misused, watermarking is generally e…
Automated Optimization Modeling via a Localizable Error-Driven Perspective
Weiting Liu, Han Wu, Yufei Kuang +4
Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivot…
Activation-Guided Consensus Merging for Large Language Models
Yuxuan Yao, Shuqi Liu, Zehua Liu +6
Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based appro…
Preserving LLM Capabilities through Calibration Data Curation: From Analysis to Optimization
Bowei He, Lihao Yin, Huiling Zhen +5
Post-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods,…
REG: A Regularization Optimizer for Robust Training Dynamics
Zehua Liu, Han Wu, Xiaojin Fu +4
Optimizers are crucial for the efficient training of Large Language Models (LLMs). While AdamW is the de facto standard, recent structure-aware optimizers like Muon have emerged, w…
A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
Ziyang Xiao, Jingrong Xie, Lilin Xu +15
By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires su…