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cs.RO2026
Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control
Hanchu Zhou, Brendan Lynch, Raman Goyal +4
World Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and robot actions, enabling the policy to learn phys- ical dyna…
cs.CL2026
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Qiyao Ma, Dechen Gao, Rui Cai +4
Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing d…