1 citations · 1 across the 13 of their papers we have counts for
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BRIDGE: Bridging Reasoning In Distillation Gap Elimination via Structure-Aware Masking
Bowen Yu, Sheng Zhang, Binhao Wang +8
Chain-of-Thought (CoT) reasoning has significantly improved LLMs' mathematical problem-solving capabilities, but distilling such capabilities into smaller models remains challengin…
DynamicPTQ: Mitigating Activation Quantization Collapse via Residual-Stream Dynamics
Zimo Zhao, Maolin Wang, Bowen Yu +3
Post-training quantization (PTQ) is essential for efficient large language model inference, but reliably quantizing activations remains challenging when weights, activations, and K…
FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement
Bingguang Hao, ZengZhuang Xu, Maolin Wang +9
The integration of large language models (LLMs) with function calling has emerged as a crucial capability for enhancing their practical utility in real-world applications. However,…
Reasoning through Exploration: A Reinforcement Learning Framework for Robust Function Calling
Bingguang Hao, Zengzhuang Xu, Maolin Wang +9
The effective training of Large Language Models (LLMs) for function calling faces a critical challenge: balancing exploration of complex reasoning paths with stable policy optimiza…
DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation
Maolin Wang, Tianshuo Wei, Sheng Zhang +6
Neural Architecture Search (NAS) has emerged as a powerful approach for automating neural network design. However, existing NAS methods face critical limitations in real-world depl…
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning
Maolin Wang, Xiangyu Zhao
There has been a significant increase in the deployment of neural network models, presenting substantial challenges in model adaptation and fine-tuning. Efficient adaptation is cru…