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Reinforcement learning (RL) allows vision-language-action (VLA) policies to generalize beyond their training distribution by optimizing directly for task success, but post-training is computationally expensive. A natural response has been to speed rollout collection through faster simulators and world models. In GRPO-based VLA RL, we find that the dominant cost lies elsewhere: gradient computation accounts for approximately 78% of wall-clock time per step in our runs, while rollout collection accounts for only 21%. Gradient cost dominates because much of […]

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Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data privacy especially with the huge increase amount of data due to the adoption of technologies which contributes to the growing number of IoT devices. Storing this amount of data centrally is challenging due to issues like limited communication, privacy, and regulations. FL can be Centralized (CFL), Decentralized (DFL), and Semi-decentralized (SDFL). Choosing the right FL architecture depends on […]

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Large language models (LLMs) keep getting bigger and better. But the cost of running them — generating text, answering questions, powering real-time applications — is scaling up, too. Obviously, model accuracy is important, but for real-time AI-based web applications, it can’t come at the expense of efficiency. In a paper we presented at the International Conference on Learning Representations (ICLR), we provide a framework for navigating this accuracy-versus-efficiency tradeoff, by connecting scaling laws directly to architectural-design decisions. The […]

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In this quiz, you’ll test your understanding of Python’s Array: Working With Numeric Data Efficiently. By working through this quiz, you’ll revisit the differences between Python’s array module and the built-in list, the meaning of type codes, how to create and manipulate arrays as mutable sequences, and the performance trade-offs of using a low-level numeric container. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every […]

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What are the limitations of using a file-based agent workflow? Why do massive context windows tend to collapse? This week on the show, Mikiko Bazeley from MongoDB joins us to discuss agentic architecture and context engineering. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

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This article explains the practical differences between AI workflows, autonomous agents, and multi-agent systems through real-world analogies, production trade-offs, and code examples. It argues that workflows are best for deterministic, structured tasks with predictable execution paths, while agents are better suited for open-ended problems requiring dynamic tool selection and adaptive reasoning. Multi-agent systems introduce specialized coordination between multiple agents but also increase operational complexity, debugging overhead, and cost. The piece also explores hybrid architectures, beginner mistakes, production reliability, […]

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