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Static parallelism layouts leave massive post-training efficiency on the table, but dynamically re-slicing distributed model states across GPUs cuts RL step latency by 28% with less than 0.1% transition overhead.
Conduit is presented, a framework-agnostic runtime that exposes RL experience management as an explicit systems optimization problem and reduces exposed experience-path latency by up to 97% and end-to-end iteration latency by up to 38%, scales to 1,024 GPUs, and preserves convergence.
