Researchers release AInfer-PD
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AInfer-PD released — points out that in agentic RL each trajectory proceeds asynchronously, alternating between generation and environment interaction over multiple turns, so the coexistence of prefill (P) and decode (D) becomes a persistent property of rollouts rather than a one-off prompt-processing event; extends in-place P/D multiplexing to distributed MoE rollouts. It coordinates the order of P/D collective communication across ranks and gives DeepEP's P and D paths independent communication state, making concurrent execution of interleaved ADP/ATP and DeepEP paths safe while sharing model weights and KV storage. Reports that on single-node prefill-heavy workloads it cut rollout completion time by 7.1–22.5% versus the same engine with P/D multiplexing turned off and by 24.8–32.9% versus SGLang, and on 2 nodes by 18.0–35.3% and 18.3–31.8% respectively
Why it matters
A strand that, instead of P/D disaggregation, which requires separate device pools and KV-cache transfers, overlaps P and D on the same devices by isolating only collective-communication order and state while sharing weights and KV. It is also a case of the serving-infrastructure bottleneck moving from inference services to RL rollouts. Figures are as reported by the authors.
Source — Primary sources first; secondary reports are labelled “Press”
Paperarxiv.orghttps://arxiv.org/abs/2609.00993Added in Week 36, 2026 · See that week
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