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

SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning arXiv:2605.23969v1 Announce Type: new Abstract: Instruction tuning has optimized the specialized capabilities of large language models (LLMs), but it often requires extensive datasets and prolonged training times. The challenge lies in developing specific capabilities by identifying useful data and efficiently fine-tuning. High-quality and diverse pruned data can help models achieve lossless performance at a low