Anyone involved in model training can likely relate to these challenges: the number of tasks keeps growing, experiment queues are getting longer, and computing bills are becoming increasingly terrifying.
Today, I’d like to share our recent open-source project: https://github.com/baidu-baige/LoongForge. What we are doing is simple: accelerating training speed while keeping the output exactly the same.
Currently, the LoongForge acceleration framework covers a batch of mainstream embodied AI models (such as pi05, GR00T, xVLA, FastWAM, Lingbot-VA, Cosmos, Dreamzero, Wall-oss-0.5, etc.). It works out of the box and generally delivers a near 200% acceleration effect—effectively cutting training time in half.
Below is a demonstration using DreamZero (an embodied AI model). Please note that this is just one example among the many models currently supported.
📊 In the demonstration video, you will notice:
✅ A 4x increase in training throughput.
✅ Loss curves that are perfectly aligned with the official baseline—not merely approximated, but exactly overlapping.
Furthermore, we welcome everyone to join us in discussing technical challenges and co-creating this open-source community. Stars, contributions, and discussions are highly welcome!
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