AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Asynchronous reinforcement learning (RL) improves the efficiency of large language model post-training but introduces stale rollouts generated by earlier policies. Theoretical understanding of how this staleness affects convergence and how to mitigate its impact remains limited. We derive a convergence bound for GRPO-s...
Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning invalid reasoning paths early, leading to significantly improved reasoning efficiency and more favorable test-time cost scaling. Despite strong empirical success, the theo...
This work forms a post-failure decision as recovery routing over heterogeneous actions and train a supervised router from execution rollouts to make the same router usable under changing budgets, and adds a Conformal Risk Control layer that selects a deployment-time cost penalty without retraining and provides marginal...
Agent reinforcement learning (RL) increasingly runs through full execution harnesses, and a multi-harness recipe mixes two choices: exposing the policy to several harnesses, and comparing their rewards inside one relative-advantage group. We isolate the second choice in repository-level coding. From one Qwen3-8B superv...
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