Show HN：LoongForge - High-performance training system for embodied models (github.com)

LoongForge has introduced a high-performance training subsystem specifically designed for embodied models, including Vision-Language-Action (VLA) policies and world-action models (WAM). Unlike traditional large models, which often exceed 10 billion parameters, these embodied models prioritize data plumbing and iteration speed over sheer parameter scale. The new subsystem leverages a torch-native Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP) engine, simplifying the training process compared to the complexity introduced by the Megatron core engine used in LoongForge’s broader architecture. This development is significant for the AI/ML community as it offers a streamlined approach to training smaller yet capable models, enhancing efficiency and performance in data-driven tasks. By separating the LoongForge-Embodied stack from the Megatron core, each can evolve independently, allowing tailored optimizations for specific workloads. The system promises easier integration of new models with uniform interfaces and configurable training parameters, alongside robust data and evaluation pipelines. With these advancements, researchers and developers can anticipate improved performance in training embodied models, facilitating breakthroughs in AI applications that require a nuanced understanding of interactions between visual and action-based inputs.