ADG selects 10K instruction examples by scoring the geometric divergence of multiple high-temperature model outputs in embedding space, outperforming prior selectors on reasoning, knowledge, and coding benchmarks across two model backbones.
It then se- lects the highest-scoring subset for SFT, improving fine-tuning efficiency without requiring an extra external scoring model
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Instruction Data Selection via Answer Divergence
ADG selects 10K instruction examples by scoring the geometric divergence of multiple high-temperature model outputs in embedding space, outperforming prior selectors on reasoning, knowledge, and coding benchmarks across two model backbones.