AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Models (GRMs). However, Mix-RL suffers from two critical bottlenecks: the reward sparsity induced by outcome rewards, and the cross-domain interference arising from jointly o...
On-policy distillation post-trains large language model agents by supplying dense, step-level guidance from a teacher policy when the reinforcement-learning reward is sparse and arrives only once per trajectory. Existing instantiations allocate this guidance by the size of the teacher-student divergence at each step, o...
RankBuffer is introduced, which maintains an ordered, query-specific buffer of previously judged responses as a reusable quality scale that achieves nearly on-par performance with the strongest ranking-based reward baseline while substantially reducing judging cost.
Fetch-then-Explore is proposed, which separates page selection from evidence extraction and keeps what it selects: pages are recorded in a per-question workspace on the filesystem rather than the context window or a transient session, and evidence is pulled from them on demand later.
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