Gradients reports that competitive, reward-driven fine-tuning beats centralized AutoML in 82 to 100 percent of comparisons, but its evaluation does not isolate competition from a much larger compute budget.
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Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation
Gradients reports that competitive, reward-driven fine-tuning beats centralized AutoML in 82 to 100 percent of comparisons, but its evaluation does not isolate competition from a much larger compute budget.