4 papers
CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning
Ji Shi, Peiming Guo, Meishan Zhang +4
Code verifiers play a critical role in post-verification for LLM-based code generation, yet existing supervised fine-tuning methods suffer from data scarcity, high failure rates, a…
Boost Post-Training Quantization via Null Space Optimization for Large Language Models
Jiaqi Zhao, Miao Zhang, Deng Xiang +3
Existing post-training quantization methods for large language models (LLMs) offer remarkable success. However, the increasingly marginal performance gains suggest that existing qu…
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
Jiaqi Zhao, Miao Zhang, Ming Wang +5
Large Language Models (LLMs) suffer severe performance degradation when facing extremely low-bit (sub 2-bit) quantization. Several existing sub 2-bit post-training quantization (PT…
Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
Jiaqi Zhao, Ming Wang, Miao Zhang +5
Post-training Quantization (PTQ) technique has been extensively adopted for large language models (LLMs) compression owing to its efficiency and low resource requirement. However,…