AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work formally identifies this limitation as High-Frequency Trajectory Collapse: supervised fine-tuning converges to the conditional mean of the training distribution, which is dominated by smooth, low-frequency textures, causing high-frequency patterns to become nearly un-sampleable.
This work proposes REST (Reward-Enhanced Scored-Trajectory Distillation), a single-stage RL-distillation co-training framework that attaches a decoupled student to an arbitrary RL teacher that enables few-step CFG-free inference that matches or surpasses its 40-step RL teacher, with an overall additional training cost...
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