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cs.CL2026
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking
Taehyeon Kim, Hyunsoo Lee, Youngsoo Jang +1
Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overthinking challenge that allocat…
cs.RO2026
Adapting Vision-Language-Action Models to Unknown Visual Disruptions During Execution
Ahin Lee, Jinwoo Seo, Youngsoo Jang +1
Visual disruptions can arise while a robot is executing a task, leaving a vision-language-action (VLA) policy to respond without knowing the disruption type or timing. We introduce…
cs.AI2026
A Regret Minimization Framework on Preference Learning in Large Language Models
Suhwan Kim, Taehyun Cho, Geon-Hyeong Kim +4
Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness sig…