Chengyao Yu

Chengyao Yu, Hao Zeng, Youxin Zhu et al.

Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks but suffer from high computational costs and latency. While selective thinking strategies improve efficiency by routing easy queries to non-thinking models, existing approaches often incur uncontrollable errors, especially in online settings where the performance loss of a non-thinking model is only partially observed and data are non-stationary. To address this, we propose Betting Probably Approximately Correct (B-PAC) reasoning, a principled method that enables anytime safe and efficient online reasoning under partial feedback. Specifically, we utilize inverse propensity scoring estimators to construct test supermartingales for candidate thresholds, and then dynamically adjust the routing threshold based on the accumulated statistical evidence of safety. Theoretically, we establish the anytime-valid performance loss control and the efficiency of B-PAC reasoning. Extensive experiments demonstrate that B-PAC reasoning significantly reduces computational overhead, decreasing thinking model usage by up to 81.01\%, while controlling the performance loss below the user-specified level.

Chenqing Lin, Mostafa Hussien, Chengyao Yu et al.

Neural network pruning is a critical compression technique that facilitates the deployment of large-scale neural networks on resource-constrained edge devices, typically by identifying and eliminating redundant or insignificant parameters to reduce computational and memory overhead. This paper proposes the Flexible Automatic Identification and Removal (FAIR)-Pruner, a novel method for neural network structured pruning. Specifically, FAIR-Pruner first evaluates the importance of each unit (e.g., neuron or channel) through the Utilization Score quantified by the Wasserstein distance. To reflect the performance degradation after unit removal, it then introduces the Reconstruction Error, which is computed via the Taylor expansion of the loss function. Finally, FAIR-Pruner identifies superfluous units with negligible impact on model performance by controlling the proposed Tolerance of Difference, which measures differences between unimportant units and those that cause performance degradation. A major advantage of FAIR-Pruner lies in its capacity to automatically determine the layer-wise pruning rates, which yields a more efficient subnetwork structure compared to applying a uniform pruning rate. Another advantage of the FAIR-Pruner is its great one-shot performance without post-pruning fine-tuning. Furthermore, with utilization scores and reconstruction errors, users can flexibly obtain pruned models under different pruning ratios. Comprehensive experimental validation on diverse benchmark datasets (e.g., ImageNet) and various neural network architectures (e.g., VGG) demonstrates that FAIR-Pruner achieves significant model compression while maintaining high accuracy.