AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to continual learning in LLMs. Recent works connect forgetting to how far, and along which directions, fine-tuning moves the weights. On-policy metho...
The per-iteration program is solved with sequential convex programming (SCP), the standard solver for difference-of-convex problems, and convergence guarantees under mild conditions are given, bridging the difference-of-convex optimization and RL literatures.
HExA shows that learning through active experimentation can help agents discover useful knowledge, acquire reusable skills, and make efficient progress on novel long-horizon tasks.
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