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cs.LG2026
Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Panagiotis Theodoropoulos, Nan Jiang, Xintong Duan +4
Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models (LLMs). High-probability seq…
cs.LG2025
SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions
Ziyi Wang, Nan Jiang, Guang Lin +1
Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight pruning or low-bit quantization indivi…