3 papers
cs.LG2026
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
Sanae Lotfi, Polina Kirichenko, Steven Li +1
Post-training quantization (PTQ) is widely used to deploy large language models efficiently, but its effect on reasoning models is not well understood. Across math, coding, and sci…
cs.CV2026
Visual Compositional Tuning
Xindi Wu, Hee Seung Hwang, Polina Kirichenko +2
Visual instruction tuning (VIT) datasets have grown rapidly in scale, yet the informativeness of individual training samples has largely been overlooked. Recent dataset selection m…
cs.LG2025
The Impact of Coreset Selection on Spurious Correlations and Group Robustness
Amaya Dharmasiri, William Yang, Polina Kirichenko +2
Coreset selection methods have shown promise in reducing the training data size while maintaining model performance for data-efficient machine learning. However, as many datasets s…