Author

Lin-Lin Yang

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#machine learning Jul 2025

Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method

Squeeze10-LLM is a staged mixed-precision post-training quantization (PTQ) framework and achieves state-of-the-art performance for sub-2bit weight-only quantization, improving average accuracy from 43% to 56% on six zero-shot classification tasks--a significant boost over existing PTQ methods.

Qingcheng Zhu, Yang-Yang Ren, Lin-Lin Yang et al. · arXiv.org · 1 citation

Jul 2026

Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning

A Kalman-Guided Prompt Selection method (KGPS), which reformulates prompt selection as a dynamic state estimation problem rather than static difficulty prediction, and consistently improves both final accuracy and rollout efficiency over strong baselines, establishing state-of-the-art performance among online prompt se...

Hao-Dong Zhu, Yang-Yang Ren, Yanjing Li et al. · arXiv.org · 2 citations · ⚡2