ATTUNE: Agentic Throughput Tuning for LLM Training
Training recipes are tuned by hand for one model, cluster and workload, and every new model, accelerator, framework version or dataset means tuning again. Automated tuners search a fixed space of knobs and check only that a job runs, yet a configuration can run to completion while it silently changes training. ATTUNE is a single coding agent that tunes any working recipe for the workload and stack at hand, unattended, within a GPU-hour budget the user sets. It diagnoses each run and changes one lever at a time, guided by a catalog of lessons from past campaigns, and ships a recipe only if it passes a correctness gate: a configuration check against objective-changing settings and known sta...
| Authors | Leo Fan, Hongyang Wang, Wenbo Chen et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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