AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language models (LLMs) have demonstrated remarkable performance across various code-related tasks. However, unlike carefully curated datasets that are typically high-quality and error-free, real-world user instructions are often vague and error-prone, posing significant challenges to the robustness of code LLMs....
This work proposes Skill-Conditioned Gated Gated Self-Distillation (SGSD), which formulates skill-based SD as teacher hypothesis validation rather than unconditional imitation, and shows that SGSD consistently improves over GRPO and remains competitive with answer-conditioned OPSD under a weaker PI assumption.
FISA is proposed, a framework for MLLM self-improvement that constructs augmented images from the model's own failure cases that generates visually challenging yet answer-preserving image complications, verifies their utility through self-examination, and applies dual fidelity filtering to avoid semantic distortion.
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