State-space models keep compact recurrent memory, but reading and writing that memory every decoding step can still dominate execution. Compact state is not automatically cheap state management.
ReplaySSM's October 9 paper represents recurrence through a checkpoint plus recent inputs. Periodic checkpoint updates reduce state traffic, while speculative rollback becomes a buffer operation. The authors implement the method in vLLM and report serving gains on tested hybrid models. Read the research paper.
Preserve the update path at higher precision
The separation also permits a lower-precision state for producing outputs while a higher-precision checkpoint handles recurrent updates. Rounding noise used for an output need not become the foundation of every later state.
This distinction matters in long streams, where small feedback errors can accumulate. Evaluate extended sequences and rollback-heavy workloads, not just isolated next-token speed.
Buffering introduces its own work and storage trade-offs. Batch size and hardware determine whether reduced state traffic outweighs additional computation.
ReplaySSM's lesson is that a model's mathematical state need not be materialised in the same form at every step. Changing when the system commits that state can improve both efficiency and numerical behaviour without changing what recurrence is meant to compute.
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