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How do you build a single model that can learn physical skills from chaotic real world robot data without relying on simulation? Generalist AI has unveiled GEN-θ, a family of embodied foundation models trained directly on high fidelity raw physical interaction data instead of internet video or simulation. The system is built to establish scaling…

How can we reliably test whether large language models actually understand Indian languages and culture in real world contexts? OpenAI has released IndQA, a benchmark that evaluates how well AI models understand and reason about questions that matter in Indian languages across cultural domains. Why IndQA? OpenAI states that about 80 percent of people worldwide…

In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model rather than relying on external orchestration. We design a compact, model-native agent that learns to perform arithmetic reasoning tasks through reinforcement learning. By combining a stage-aware actor-critic network with a curriculum of increasingly complex environments,…

How can consistency training help language models resist sycophantic prompts and jailbreak style attacks while keeping their capabilities intact? Large language models often answer safely on a plain prompt, then change behavior when the same task is wrapped with flattery or role play. DeepMind researchers propose consistent training in a simple training lens for this…

In this tutorial, we explore Hydra, an advanced configuration management framework originally developed and open-sourced by Meta Research. We begin by defining structured configurations using Python dataclasses, which allows us to manage experiment parameters in a clean, modular, and reproducible manner. As we move through the tutorial, we compose configurations, apply runtime overrides, and simulate…

Code-oriented large language models moved from autocomplete to software engineering systems. In 2025, leading models must fix real GitHub issues, refactor multi-repo backends, write tests, and run as agents over long context windows. The main question for teams is not “can it code” but which model fits which constraints. Here are seven models (and systems…

Can large language models collaborate without sending a single token of text? a team of researchers from Tsinghua University, Infinigence AI, The Chinese University of Hong Kong, Shanghai AI Laboratory, and Shanghai Jiao Tong University say yes. Cache-to-Cache (C2C) is a new communication paradigm where large language models exchange information through their KV-Cache rather than…

How can AI teams run Tinker style reinforcement learning on large language models using their own infrastructure with a single unified engine? Anyscale and NovaSky (UC Berkeley) Team releases SkyRL tx v0.1.0 that gives developers a way to run a Tinker compatible training and inference engine directly on their own hardware, while keeping the same…

One of the biggest challenges in real-world machine learning is that supervised models require labeled data—yet in many practical scenarios, the data you start with is almost always unlabeled. Manually annotating thousands of samples isn’t just slow; it’s expensive, tedious, and often impractical. This is where active learning becomes a game-changer. Active learning is a…

In this tutorial, we explore how to build an intelligent agent that remembers, learns, and adapts to us over time. We implement a Persistent Memory & Personalisation system using simple, rule-based logic to simulate how modern Agentic AI frameworks store and recall contextual information. As we progress, we see how the agent’s responses evolve with…