UPMs apply periodic time-varying random invertible transforms to sharded model components in decentralized setups to render cross-time assemblies incoherent while preserving network function and incurring minimal overhead.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SparseRL-Sync achieves lossless weight synchronization in large-scale RL by sending only changed parameters, reducing communication volume by roughly 100x under observed 99%+ element-level sparsity.
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Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
UPMs apply periodic time-varying random invertible transforms to sharded model components in decentralized setups to render cross-time assemblies incoherent while preserving network function and incurring minimal overhead.
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SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication
SparseRL-Sync achieves lossless weight synchronization in large-scale RL by sending only changed parameters, reducing communication volume by roughly 100x under observed 99%+ element-level sparsity.