AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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D-MOPD (Dynamic Domain ScheDuling for MOPD), a zero-overhead scheduler that repurposes the per-domain reverse-KL signal already produced during training to adapt the domain mixture online to adapt the domain mixture online.
This work proposes SABER, a training-free framework for stability-aware early exit via adversarial branch probing, and shows that SABER reduces reasoning token consumption by 30.2% on average while maintaining competitive accuracy with full-length reasoning.
As the context window of Large Language Models (LLMs) continues to expand, the data required to effectively train and evaluate these capabilities remains underexplored. With existing research primarily focuses on architectural optimization, there is a need for a systematic, data-centric review. This survey bridges th...
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