字节跳动发布高效预训练长度缩放技术，突破长序列训练瓶颈
字节跳动宣布推出高效预训练长度缩放技术（Efficient Pretraining Length Scaling），通过创新的Parallel Hidden Decoding Transformer(PHD-Transformer)框架，显著提升大语言模型(LLM)在长序列预训练中的效率与性能。据AIbase了解，该技术在保持推理效率的同时，支持高达2048K(2M)的上下文长度训练，解决了传统框架在数据异构性与计算平衡上的瓶颈。相关研究已在arXiv公开，引发了AI研究社区的广泛关注。核心创新:PHD-Transformer优化长序列训练字节跳动的PHD-Transformer通过独特的键值缓存（KV Cache）管理策略与架构优
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