2 papers
cs.CV2026
Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models
Zhikai Li, Yue Zhao, Edward Zhongwei Zhang +4
Reinforcement learning from human feedback (RLHF) effectively promotes preference alignment of text-to-image (T2I) diffusion models. To improve computational efficiency, direct pre…
cs.RO2020
Sampling Training Data for Continual Learning Between Robots and the Cloud
Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya +5
Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road constructi…