AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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PEARL is an asynchronous agentic RL system that coordinates external resource elasticity, temporary reuse of idle training GPUs, and adaptive PD execution, and maintains a unified GPU--worker--role state and uses runtime profiles to predict rollout batch completion time.
W EAVE is a cluster scheduling framework built on the insight that the structural idleness of one job can be effectively utilized by the active phase of another, and introduces the co-execution group abstraction, which partitions the cluster into isolated locality domains.
PCoMoE is presented, a path-compositional execution framework that shifts MoE inference from coarse-grained expert selection to fine-grained path composition and achieves up to a 1.31x end-to-end inference speedup while enhancing model accuracy by 10%.
Rollplex is presented, a runtime that decomposes the reference and training phase and moves the prefix computation into the rollout decode window and achieves speedup over serial colocation and disaggregation under the same GPU budget, while preserving the synchronous RL update.
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