AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 12 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.
Experiments on reasoning, code-generation, scientific-knowledge, scientific-knowledge, and tool-use benchmarks show that these implementations can be executed through the same verl-based backend while retaining their method-specific objectives and task-dependent performance profiles.
Self-Evaluation Elicitation (SEE) is introduced, a method that surfaces a latent ability to predict how a judge will score its own output through a short cycle comprising a calibration-coupled reinforcement learning phase that improves the answer and predicts the judge, followed by a masked distillation phase that sharpens the prediction while leaving the answer untouched.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.