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cs.CL2026
The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization
Chao Li, Shigeng Wang, Anbang Yao
Post-training quantization (PTQ) methods typically use sequential quantization that partitions a pre-trained LLM into a series of units (e.g., transformer blocks), with one unit qu…
cs.RO2026
Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models
Jiawei Fan, Sifeng Wang, Yuqing Hou +1
In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency of multi-step flow matching. Me…