AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work modify RoPE by learning a scalar per frequency, treating frequencies as learnable parameters rather than hyperparameters, and observes and analyzes the emergence of a high-norm, positional LeRoPE band.
The results support on-policy self-distillation as an efficient and analyzable approach to diffusion post-training by converting image-level reward guidance into explicit and continually refreshed intermediate supervision, thereby opening a path toward more efficient and diagnosable alignment.
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