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University of Maryland, College Park
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Achieving up to 6× greater sample efficiency in diffusion RLHF by strategically reweighting timesteps and reusing informative trajectories could revolutionize how we align generative models with human preferences.
TeCoNeRV achieves state-of-the-art video compression with INRs, finally making hypernetwork-based approaches practical for high-resolution videos (up to 1080p) by slashing memory overhead and bitrate.
Stable Diffusion can serve as a surprisingly effective, instruction-aware visual encoder for MLLMs, outperforming CLIP on tasks requiring spatial and compositional reasoning.