, SeaCache) does not reliably predict the eventual quality loss in the final video (terminal error).
RL-trained cache scheduler adapts video diffusion to user-specified speedups
The method uses latent features and offline-to-online reinforcement learning to minimize final video quality loss at any desired acceleration ratio.
Chinese Tech
Yuxiang Xiong · Ruiyan Wang · Wenqiang Wang · Teng Hu · Songhang Shen · Bohao Feng · +2 more
Shanghai Jiao Tong University · Alibaba Group
Research Digest··3 min read
Authors propose MORCA, a cache scheduling framework that treats cache decisions as a Markov decision process and optimizes them via reinforcement learning.
Why this paper
From Alibaba Group and Shanghai Jiao Tong University
In one line
MORCA uses latent-aware offline-to-online reinforcement learning to schedule cache reuse, improving video diffusion fidelity at user-specified acceleration targets over existing caching methods.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ✓Limitations stated by the authors
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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