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Research questionHow can batched LLM serving handle request-specific read-write adaptation state without corrupting streams or losing throughput?Standard batched LLM serving assumes shared static weights, but test-time training changes request-owned state during generation. Mixing incompatible reads and writes can transfer state across streams, while serial execution limits throughput.
AI
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Inference Optimization
LLM Pretraining & Post-training
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Latest papersRecent research connected to this question, newest first.RW-TTT: Batched Serving for Request-Owned Test-Time Training StateThe source concerns read-write test-time training during generation, including request-owned fast weights, low-rank deltas, and streaming learner state. Evidence is reported on one GPU with eight InPlace-TTT streams, using aggregate throughput, RULER behavior, and owner/version checks; broader hardware and workload coverage is not specified.research paper · Sep 3, 2026
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