RW-TTT: Batched Serving for Request-Owned Test-Time Training State

Abstract:Test-time training (TTT) adapts an LLM during generation by reading and updating request-owned state, such as fast weights, low-rank deltas, or streaming learner state. This breaks batched LLM serving, which assumes shared static weights: serial execution is correct but slow, while naive batching can corrupt request state. We formulate this problem as read-write TTT serving and present RW-TTT , which tags each decode step with its owner, version, and READ/WRITE effect, batches only compatible phases, and commits updates only to the owner. On one GPU with eight fast-weight InPlace-TTT streams, RW-TTT reaches 274.61 aggregate tok/s, 9.31x over sequential serving and 3.44x over per-stream replicas under the same memory budget. It preserves behavior on RULER, a long-context benchmark, and passes owner/version checks.

Machine Learning (cs.LG)

arXiv:2605.28053 [cs.LG]

(or arXiv:2605.28053v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.28053 Focus to learn more arXiv-issued DOI via DataCite

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