AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 3 of 15 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Envs-FORGE is presented, a prompting policy that converts verifier rewards into per-seed environment-synthesis actions and solves a per-seed mixed-integer linear program (MILP) to choose the action that conditions generation.
The DataFoundry is introduced, a framework for evolving data preparators through recursive self-improvement before large-scale data production, and it is found that recursively evolved preparators produce training data with higher downstream utility than baselines.
LazyTrain is proposed, an optimization layer over a layer-streaming executor that formulates checkpoint selection, activation placement, recomputation, and CPU-GPU-NVMe communication overlap as a mixed-integer scheduling problem, then executes the solved policy during training.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.