BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//PyTorch - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://pytorch.org
X-WR-CALDESC:Events for PyTorch
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250806T110000
DTEND;TZID=America/Los_Angeles:20250806T120000
DTSTAMP:20260426T235628
CREATED:20250707T201254Z
LAST-MODIFIED:20250811T204034Z
UID:10000040-1754478000-1754481600@pytorch.org
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250814T100000
DTEND;TZID=America/Los_Angeles:20250814T110000
DTSTAMP:20260426T235628
CREATED:20250718T163422Z
LAST-MODIFIED:20250821T013230Z
UID:10000042-1755165600-1755169200@pytorch.org
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
END:VEVENT
END:VCALENDAR