AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Language model agents can be improved by updating their model weights or refining the harness that guides task execution. These components are coupled: weight updates change how the model uses the harness, while harness updates change the trajectories used for training. We propose VACE, Validation-Gated Alternating CoE...
This paper introduces HarnessEvolve, a self-evolving framework that learns from reference trajectories to achieve reliable agent self-evolution, and overcomes credit assignment failure by generating reference trajectories and aligning failed executions against them to extract error signals.
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