Papers by Weizhong Zhang
Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies (2026.findings-acl)
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Changhai Zhou, Shiyang Zhang, Yuhua Zhou, Jun Gao, Qian Qiao, Shichao Weng, Weizhong Zhang, Cheng Jin
| Challenge: | Existing quantization-aware fine-tuning methods decouple weight precision and adapter capacity, overlooking that a layer’s ability to adapt is constrained by the information preserved in its frozen weights. |
| Approach: | They propose a framework that jointly optimizes per-layer quantization bit-width and LoRA rank. |
| Outcome: | Experiments on LLaMA and Qwen models show that the proposed framework matches or approaches 16-bit baselines while using substantially less memory. |
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient (2025.acl-long)
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| Challenge: | Recent pruning methods rely on heuristically hand-crafted metrics, leading to suboptimal performance. |
| Approach: | They propose a method that optimizes pruning masks by minimizing back-propagation . they learn an underlying Bernoulli distribution to sample binary pruning mask samples . |
| Outcome: | The proposed method is able to support global and heterogeneous pruning without back-propagation. |
TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data (2025.findings-naacl)
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| Challenge: | Existing methods for instruction tuning are limited due to the increasing volume of instruction datasets and the increased computational costs. |
| Approach: | They propose to extract a small and highly informative subset of training samples from a large dataset that achieves comparable performance to the full dataset. |
| Outcome: | The proposed algorithm outperforms other unsupervised methods and achieves comparable performance to the full dataset. |