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cs.LG2026
Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF
Eric Zhu, Abhinav Shrivastava, Soumik Mukhopadhyay
Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences. However, applying RLHF to diffusion mode…
cs.LG2019★ 8 cited
Scalable Model Compression by Entropy Penalized Reparameterization
Deniz Oktay, Johannes Ballé, Saurabh Singh +1
We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a "latent" space, amounting to…