AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 3 of 11 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.
These experiments provide empirical evidence that reasoning gains from gameplay can yield stronger transfer than math or code, and highlight GlyphBench's value as a testbed for systematic research on how language-model agents learn, interact, and generalize.
Agentick's capability-decomposed, multi-modal design provides the empirical infrastructure needed to drive progress toward general autonomous agents, both as an evaluation framework and as a training ground for RL post-training of foundation models in truly sequential environments.
The resulting algorithm reduces training-time falls by factors of 233x, 48x, and 26x on HalfCheetah, Ant, and Unitree Go1 over standard PPO, while matching or exceeding PPO's final reward, and on Ant, where the recovery policy is unreliable, it is the only method that reaches 80% of the best final reward.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.