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Fine-grained data balancing in heterogeneous pipelines unlocks 2x training speedups for large models by overcoming memory bottlenecks and coarse workload assignments.
psRL can boost training throughput by over 5x by efficiently sharing prefixes among samples, addressing a critical bottleneck in agentic AI training.