AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 4 of 87 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
AD-WM, an action-discriminative joint-embedding world model for counterfactual MPC, is introduced, which combines residual latent dynamics with predictor-level action-recovery regularization, using inverse dynamics and a normalized recovery objective motivated by conditional mutual information.
Direct Advantage Amplification (DAA), which amplifies the advantages of hard-to-sample correct responses on hard prompts, as obtained by Dynamic Sampling, is proposed, which ensures that, when Dynamic Sampling is used, these hard-to-sample responses can be effectively capitalized on, implying higher training efficiency...
RA-OPD selects more reliable trajectories to improve student model performance without requiring additional computational cost and is evaluated on math and code benchmarks using models from the Qwen3 family and the DeepSeek-R1 family.
This work advocates for Joint Online-Offline Fine-Tuning as a superior paradigm that breaks the convention of restricting offline data to SFT and online data to RFT, and provides the first comprehensive survey focusing specifically on the synchronization of data provenance.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.