Instruction Tuning

SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning

This research introduces SLAP, a novel batch-aware data selection framework designed to improve the data efficiency of instruction tuning for LLMs. SLAP optimizes learning by evaluating entire batch compositions, ensuring comprehensive data distribution coverage and maximizing intra-batch diversity to achieve lossless performance with reduced training costs.