In the digital world, the growth of artificial intelligence relies on checkpoints, which can restore the training status to the last safe state during training large language models (LLMs) if issues like sudden power outages or hardware failures occur. However, traditional checkpoint systems are inefficient when dealing with large models. To address this, ByteDance and the research team from Hong Kong University have proposed ByteCheckpoint, an innovative checkpoint system that improves data and metadata handling.