TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts
TailSieve proposes a partial‑rollout‑guided framework that jointly manages tail routing and replica allocation for large‑scale LLM rollouts, aiming to reduce the makespan caused by a few long‑tail generations. By isolating these long tasks while balancing load across replicas, the method seeks to optimize overall throughput in reinforcement learning, on‑policy distillation, and evaluation pipelines. The approach is illustrated in an idealized setting where completion lengths are known, demonstrating the benefits of combining tail isolation with load balancing.
TailSieve proposes a partial‑rollout‑guided framework that jointly manages tail routing and replica allocation for large‑scale LLM rollouts, aiming to reduce the makespan caused by a few long‑tail generations. By isolating these long tasks while balancing load across replicas, the method seeks to optimize overall throughput in reinforcement learning, on‑policy distillation, and evaluation pipelines. The approach is illustrated in an idealized setting where completion lengths are known, demonstrating the benefits of combining tail isolation with load balancing.
Sources
- arXiv cs.LG — TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts
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