Author: Manoj Balakrishnan

   Reinforcement Learning RL post-training is now a major lever for reasoning-centric LLMs, but unlike pre-training, it hasn’t had predictive

   In this tutorial, we build a compact, efficient framework that demonstrates how to convert tool documentation into standardized, callable

   How do you convert complex, multilingual documents—dense layouts, small scripts, formulas, charts, and handwriting—into faithful structured Markdown/JSON with state-of-the-art

   A team of researchers from Google Research, Google DeepMind, and Yale released C2S-Scale 27B, a 27-billion-parameter foundation model for

   In this tutorial, we build an AI-powered cryptographic agent system that combines the strength of classical encryption with adaptive

   In 2025’s transformation of outbound sales, demand gen, and partnership outreach, the stakes for campaign ROI and sender reputation

   Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn

   If you’ve ever tried to manage multiple Instagram pages, test ads in different countries, or just keep your browsing

   What would you build if you could run Reinforcement Learning (RL) post-training on a 32B LLM in 4-bit NVFP4—on

   In this tutorial, we explore how to build a Context-Folding LLM Agent that efficiently solves long, complex tasks by