DomainPilot: Efficient LLM Fine-Tuning via Domain-Level Data Optimization

The recently introduced DomainPilot framework tackles the optimization of data mixtures for fine-tuning large language models. It features token-level monitoring of domain loss, enabling the observation of learning dynamics for each domain while maintaining an uninterrupted data flow. Guided by a Scaling Law, a preliminary optimization phase aligns with domain-specific convergence patterns, resulting in a systematic data mixture. This innovative method significantly lowers O(N) expenses and alleviates I/O constraints in comparison to current techniques. The research is available on arXiv, listed under ID 2607.22769.