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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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