AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Reward models (RMs) are a key component of large language model post-training, providing reward signals for subsequent reinforcement learning. However, conventional discriminative RMs typically output only scalar scores, making it difficult to identify the response behaviors associated with their scoring decisions. Exi...
Comprehensive empirical evaluations demonstrate PEAR significantly improves average accuracy over the strongest debate baselines, and theoretically characterize PEAR as an equivariant sparse router: it preserves accuracy under agent relabeling while reducing routing complexity and improving generalization.
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