AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes DRIFT (Data Refinement via On-Policy Influence Functions for Supervised Fine-Tuning), a data attribution method built on influence functions that outperforms all evaluated data selection baselines on Olmo3-7B-Instruct-SFT and OpenR1-Distill-7B, raising average accuracy across eight benchmarks.
This work introduces EvolveScaler, a code-driven framework that defines information evolution before rendering it as natural language, and shows that code-driven IE synthesis provides both challenging evaluation and transferable training supervision.
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