AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Liminal training is introduced, an annealed KL-regularized fine-tuning method that constrains early drift from the base model and shows that KL timing matters: early regularization is more effective than late regularization, and sweeping the regularization strength reveals an empirical trade-off between task learning a...
This work presents two converging lines of evidence that linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accuracy in predicting answer correctness compared to SFT models, indicating more linearly separable and structured representations.
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