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cs.LG2025
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Wei Huang, Yi Ge, Shuai Yang +11
We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-i…
cs.LG2017★ 230 cited
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Huizi Mao, Song Han, Jeff Pool +4
Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accel…
cs.LG2016★ 746 cited
Trained Ternary Quantization
Chenzhuo Zhu, Song Han, Huizi Mao +1
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with lim…