AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Across multi-hop QA and long-horizon web reasoning tasks, StepKV sustains accuracy under low KV budgets where token-level baselines degrade sharply, offering a more robust efficiency-accuracy trade-off for multi-step agent inference.
This work proposes a novel training framework that explicitly aligns the LLM-generated judgment distribution with human evaluation distributions, and incorporates adversarial training to ensure a robust alignment with this true distribution, rather than overfitting to its imperfect approximation.
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