Core Viewpoint - The article discusses the development of OpenTau, an open-source training toolchain for VLA models, aimed at improving reproducibility, usability, and scalability in model training [1]. Group 1: Industry Pain Points - Existing VLA model training tools like OpenPi and LeRobot lack a one-stop solution, with significant core capabilities missing, failing to meet the advanced training needs of VLA models [3]. - There are issues with mixed data training, as OpenPi and LeRobot do not support heterogeneous datasets with adjustable mixed ratios for collaborative training, discrete action training, or knowledge isolation between VLM and action decoders [3][4]. Group 2: OpenTau Framework Enhancements - OpenTau expands on LeRobot (PyTorch framework), ensuring full compatibility with the LeRobot ecosystem, allowing for the reuse of compliant strategies and datasets [5]. - The framework addresses the limitations of OpenPi by providing native support for the Dropout layer in PyTorch, which was previously only available in Jax [5][6]. - OpenTau improves checkpoint completeness by supplementing the missing text embeddings from LeRobot, ensuring the integrity of model functionality [7]. Group 3: Key Features and Modules - OpenTau supports heterogeneous datasets for collaborative training with adjustable mixing ratios [8]. - New features include discrete action training capabilities, knowledge isolation between VLM backbone and action decoders, and the integration of a Dropout layer to reduce overfitting risks [12]. - The framework includes a built-in reinforcement learning pipeline, supports multi-node and multi-GPU distributed training, and is compatible with simulation environments for model evaluation [12].