arXiv:2508.12116v2 Announce Type: replace-cross Abstract: As numerous instruction-tuning datasets continue to emerge, dynamically balancing and optimizing their mixtures has become a critical challenge. To address this, we propose DynamixSFT, a dynamic and automated method for instruction-tuning dataset mixture optimization. We formulate the problem as a multi-armed bandit setup and introduce a Prior-scaled Boltzmann Exploration that softly anchors the updated sampling distribution to the original dataset proportions, thereby preserving the inherent diversity and coverage of the collection. Sa
The proliferation of instruction-tuning datasets necessitates more sophisticated and automated methods to optimize their use as large language models mature.
Efficiently combining diverse instruction datasets is crucial for developing more capable and general-purpose AI models, impacting the overall advancement of AI.
The development and fine-tuning of AI models could become more automated and less reliant on manual dataset curation, leading to faster iteration cycles and improved model performance.
- · AI developers
- · Large Language Models
- · AI research institutions
- · Data scientists
- · Manual dataset curation processes
- · Inefficient AI development pipelines
Improved performance and broader applicability of instruction-tuned AI models.
Accelerated development of more generalized and robust AI systems across various applications.
Enhanced AI capabilities contributing to breakthroughs in fields powered by advanced AI, potentially enabling more complex AI agentic systems.
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