AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Data-centric scaling of RLVR has focused on collecting or generating verifiable training problems. However, corpus size alone does not determine whether the dataset provides a useful learning signal for a target policy. To guide the synthesis of effective RLVR data, we experimentally characterize dataset effectiveness...
The results suggest that the diversity of the training environment, not only the policy, is critical to the generalization of multi-turn RL to real-world deployment, and releases SCOPE, an open-source framework for Population Co-Training multi-agent RL.
Zone of Proximal Policy Optimization (ZPPO), inspired by Vygotsky's zone of proximal development, is introduced, which outperforms off/on-policy distillation and GRPO, with the largest gains at the smallest scale.
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