DeepMind Shows Smaller Models Generate Better Synthetic Reasoning Data Than Larger Ones
A new DeepMind paper demonstrates that compute-matched sampling from smaller models produces higher-quality synthetic training data, with gains reaching 31.6% — a finding that could reshape how labs approach data generation.
DeepMind published research this week showing that smaller models, when given equivalent compute budgets, produce better synthetic reasoning data than their larger counterparts. As @LiorOnAI reported, training gains from this approach reached 31.6% in some benchmarks.
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