AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The conclusion is not that LLM rerankers are robust, but that negative distributions are part of the retriever-reranker interface and should be checked before deployment.
The conclusion is not that LLM rerankers are robust, but that negative distributions are part of the retriever-reranker interface and should be checked before deployment.
Reinforcement learning with verifiable rewards (RLVR) controls policy entropy by reweighting tokens against an external scalar advantage. On-policy distillation (OPD) faces the same surface problem, a student that can sharpen too fast, but it replaces that advantage with a per-token teacher target. That shared symptom...
Learning from interaction experience is essential for enabling large language models to continually improve during deployment. Context distillation offers a promising path: privileged information extracted from experience is provided to a teacher, whose behavior is then distilled into a student model that has no access...
This work proposes Astar, a training-based approach that learns a specialized evolution-guiding model from the abundant iteration histories of industrial systems, and proposes a pipeline that turns noisy historical commits into a large, clean evolutionary corpus via pairwise sample expansion and noise filtering.
Unified On-Policy Self-Distillation (USD), a lightweight online algorithm to solve the Lagrangian, reveals that a single dual variable governs both decisions: at one price for learning difficulty, it simultaneously sets the token-selection threshold and the direction of privileged-information adjustment, keeping superv...
This survey examines agentic artifact creation, which is defined as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work, and formulate principles for keeping commitments and responsibility explicit, turning feedback into targeted r...
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