AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 4 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.
Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large language models on open-ended tasks. However, standard methods compress criterion-level feedback into scalar rewards and assign uniform token advantages, leaving structured r...
Outcome-Guided On-Policy Distillation is proposed, which applies trajectory-relative weighting to teacher supervision and calibrates these weights using final task outcomes from paired student continuations and selectively strengthens supervision on the student's original trajectories at turns where teacher guidance be...
DE-Venus is presented, a unified framework for data-efficient RLVR that treats supervision as evolving state across data preparation and policy optimization and reduces annotation and training costs without sacrificing scalable RL execution.
NapMem is introduced, a framework for learning to use long-term user memory as a structured action space rather than passively retrieved context, and suggests that long-term user memory benefits from coupling structured storage with a learned policy for using memory at the appropriate granularity.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.