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cs.AI2025
Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set
Chengwei Wu, Li Du, Hanyu Zhao +4
Scaling the amount of data used for supervied fine-tuning(SFT) does not guarantee the proportional gains in model performance, highlighting a critical need to understand what makes…
cs.AI2025
Scaling Towards the Information Boundary of Instruction Sets: The Infinity Instruct Subject Technical Report
Li Du, Hanyu Zhao, Yiming Ju +1
Instruction tuning has become a foundation for unlocking the capabilities of large-scale pretrained models and improving their performance on complex tasks. Thus, the construction…