Papers with instruction-tuning dataset mixture optimization

    1 papers
    DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections (2026.findings-acl)

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    Challenge: Several studies rely on additional models to optimize mixtures.
    Approach: They propose a method that dynamically optimizes instruction-tuning dataset mixtures by prior-scaled Boltzmann Exploration and a multi-armed bandit setup.
    Outcome: The proposed method improves the TÜLU-2-mixture and TÜLO-3-mixtures across 10 benchmarks while introducing minimal computational overhead over naive sampling.

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