AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The novelty is twofold: tying curriculum design to the non-degeneracy of the RLOO advantage, and a zero-overhead adaptive sampler that discovers difficulty from reward rather than from a hand-set proxy.
AdaKP is an online selector that re-chooses each problem's KP subset over the course of RL training, an entropy proxy that scores a KP by the reduction in next-token entropy it induces in a single inexpensive forward pass, with a provable bound on its truncation bias.
Desc descriptive evidence is provided that long-horizon multi-tool post-training can change ways of working that transfer beyond its training domain, and both software-engineering benchmarks improve despite the training collection containing no software-engineering tasks.
Off-Context GRPO (OC-GRPO), a minimally modified variant of GRPO that uses guided rollouts but applies an importance-corrected objective to steer the update back toward the original unguided objective, avoiding the mismatch that destabilizes uncorrected guided training.
This work makes execution time learnable through three stages: how code is tested, by building DMC-Optim with large optimization tests and a calibrated sandbox; how speed is turned into reward, by composing correctness and speed in the RL environment and using an offline simulator to predict the most promising configur...
A controlled study isolates the source of MADA-RL's gains: the counterfactual advantage produces the highest critic improvement rate of any model evaluated, indicating that trained critics learn to correct generator errors rather than to imitate them.
This work proposes RLPF, reinforcement learning from performance feedback, which turns execution outcomes into a staged reward, and suggests that code agents can be trained not only to pass tests, but also to optimize the programs they write.
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