AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper studies a focused question: whether a small amount of targeted supervision can improve a tool agent’s choice of immediate recovery actions under controlled failures, and introduces Mini Recovery Tuning, a deterministic benchmark and training protocol for evaluating this question.
An action-aware reinforcement learning method that combines a per-turn reward sequence rewarding both the discovery and commitment of gold code regions with an action-level advantage estimation scheme that isolates each action's credit by grouping turns sharing the same exploration context is proposed.
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