AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper proposes goal-conditioned supervised learning (GCSL) as an offline fine-tuning framework for LLMs and proposes natural-language goal representations to further connect these patterns to the LLM's pretrained knowledge and generalization capabilities.
RRCM is proposed, a ranking-driven retrieval-and-reasoning framework over collaborative and metadata memories for LLM-based agentic recommendation that significantly outperforms traditional baselines and diverse LLM-based recommendation approaches.
Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable tasks, where case-specific evidence and domain knowledge determine a checkable answer, self-play requires generating plausible cases whose answers can be independently verifi...
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