38 citations · 72 across the 29 of their papers we have counts for
Showing 2026 · cs.LGShow all
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cs.LG2026
Widening the Gap: Exploiting LLM Quantization via Outlier Injection
Xiaohua Zhan, Kazuki Egashira, Robin Staab +2
LLM quantization has become essential for memory-efficient deployment. Recent work has shown that quantization schemes can pose critical security risks: an adversary may release a…
cs.LG2026
Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR
Kazuki Egashira, Mark Vero, Jasper Dekoninck +3
Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs). While RLVR is designe…