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SUMMARY:verl: Flexible and Scalable Reinforcement Learning Library for LLM Reasoning and Tool-Calling
DESCRIPTION:Speaker: Haibin Lin \n\nverl is a flexible and efficient framework for building end-to-end reinforcement learning pipelines for LLMs. It provides a user-friendly hybrid-controller programming model\, supporting various algorithms such as PPO/GRPO/DAPO with effortless scaling. Recent trends in reasoning models bring new challenges to RL infrastructure\, such as efficient tool calling\, multi-turn interactions\, and capability to scale up to giant MoE models like DeepSeek. To lower the barrier to RL for advanced reasoning and tool calling\, we improve verl with (1) efficient request level async multi-turn rollout and tool calling\, (2) integration with expert parallelism for large scale MoE models\, (3) async system architecture for off-policy / async RL algorithms and flexible device placement.\n\n\n\n\nHaibin Lin works on LLM infrastructure at Bytedance Seed\, focusing on optimizing training performance for LLMs & multimodal understanding and generation models on large scale clusters\, from pre-training to post-training. Before he joined Bytedance\, he was working on Apache MXNet (training\, inference\, runtime\, and recipes like gluon-nlp).\n\n\n\nLinkedIn\nGitHub
URL:https://pytorch.org/event/verl-flexible-and-scalable-reinforcement-learning-library-for-llm-reasoning-and-tool-calling/
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2025/07/Haibin-Lin.png
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DTSTART;TZID=America/Los_Angeles:20250814T100000
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SUMMARY:PyTorch 2.8 Live Release Q&A
DESCRIPTION:Our PyTorch 2.8 Live Q&A webinar will focus on PyTorch packaging\, exploring the release of wheel variant support as a new experimental feature in the 2.8 release. This feature is designed to improve the PyTorch install experience for users once it becomes generally available. \nCharlie is the founder of Astral\, whose tools like Ruff—a Python linter\, formatter\, and code transformation tool—and uv\, a next-generation package and project manager\, have seen rapid adoption across open source and enterprise\, with over 100 million downloads per month. \nJonathan has contributed to deep learning libraries\, compilers\, and frameworks since 2019. At NVIDIA\, Jonathan helped design release mechanisms and solve packaging challenges for GPU-accelerated Python libraries. A founding force behind WheelNext\, Jonathan actively works on proofs of concept\, demos\, and PEPs. \nRalf is CEO\, Technology at Quansight and a long-time maintainer of NumPy and SciPy. With over 15 years in the scientific Python ecosystem\, Ralf also maintains meson-python\, created the Array API standard and pypackaging-native\, and focuses on building sustainable open source communities. \nEli Uriegas is a Staff Software Engineer at Meta and a key contributor to the PyTorch project. Eli focuses on improving the developer experience through infrastructure enhancements and the application of AI to developer tools\, and is a maintainer of PyTorch’s build and CI systems \nWatch on demand on YouTube.
URL:https://pytorch.org/event/pytorch-live-2-8-release-qa/
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2025/07/2.8-1-1.png
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20251022
DTEND;VALUE=DATE:20251024
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UID:10000001-1761091200-1761263999@pytorch.org
SUMMARY:PyTorch Conference 2025
DESCRIPTION:Join us in San Francisco on October 22-23\, 2025 to learn about AI and PyTorch\, the cutting-edge renowned open source machine learning framework. This two-day event that brings together top-tier researchers\, developers\, and academic communities\, fostering collaboration and advancing end-to-end machine learning.
URL:https://pytorch.org/event/pytorch-conference-2025/
CATEGORIES:PyTorch-hosted
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