ML-for-ML

• URL: http://arxiv.org/abs/2608.06046v1
• Date: Thu, 06 Aug 2026 13:57:23 GMT
• Title: ML-for-ML
• Abstract summary: We present ML-for-ML, a cross-layer perspective in which network-side and ML-side knobs are selected jointly under a shared time-to-target-loss objective.<n>Our preliminary prototype shows that by co-optimizing the ML and network parameters, we reach the target loss up to 42% faster.
• Score: 6.120023364420331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important. In shared cloud clusters, training and fine-tuning jobs compete with co-running workloads for network resources, while network mechanisms and ML training choices are typically optimized separately: networking controls how bytes move, whereas ML systems control when and how much communication occurs. We argue that this separation leaves end-to-end performance on the table. We present ML-for-ML, a cross-layer perspective in which network-side and ML-side knobs are selected jointly under a shared time-to-target-loss objective. Our preliminary prototype shows that by co-optimizing the ML and network parameters, we reach the target loss up to 42% faster.

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