title: Yi-Lightning Technical Report
summary: This technical report presents Yi-Lightning, our latest flagship large
  language model (LLM). It achieves exceptional performance, ranking 6th overall
  on Chatbot Arena, with particularly strong results (2nd to 4th place) in
  specialized categories including Chinese, Math, Coding, and Hard Prompts.
  Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture,
  featuring advanced expert segmentation and routing mechanisms coupled with
  optimized KV-caching techniques. Our development process encompasses
  comprehensive pre-training, supervised fine-tuning (SFT), and reinforcement
  learning from human feedback (RLHF), where we devise deliberate strategies for
  multi-stage training, synthetic data construction, and reward modeling.
  Furthermore, we implement RAISE (Responsible AI Safety Engine), a
  four-component framework to address safety issues across pre-training,
  post-training, and serving phases. Empowered by our scalable super-computing
  infrastructure, all these innovations substantially reduce training, deployment
  and inference costs while maintaining high-performance standards. With further
  evaluations on public academic benchmarks, Yi-Lightning demonstrates
  competitive performance against top-tier LLMs, while we observe a notable
  disparity between traditional, static benchmark results and real-world, dynamic
  human preferences. This observation prompts a critical reassessment of
  conventional benchmarks' utility in guiding the development of more intelligent
  and powerful AI systems for practical applications. Yi-Lightning is now
  available through our developer platform at https://platform.lingyiwanwu.com.
paper_authors._id: 674e6cb0288854089119b0be
paper_authors.name: 01. AI
paper_authors._id: 674e6cb0288854089119b0c0
paper_authors.name: Alan Wake
paper_authors._id: 674e6cb0288854089119b0c1
paper_authors.name: Albert Wang
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paper_authors.fullname: Guoyin Wang
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paper_authors.user: guoyinwang
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paper_authors.name: Bei Chen
paper_authors._id: 674e6cb0288854089119b0c3
paper_authors.name: C. X. Lv
paper_authors._id: 674e6cb0288854089119b0c4
paper_authors.name: Chao Li
paper_authors._id: 674e6cb0288854089119b0c5
paper_authors.name: Chengen Huang
paper_authors._id: 674e6cb0288854089119b0c6
paper_authors.name: Chenglin Cai
paper_authors._id: 674e6cb0288854089119b0c7
paper_authors.name: Chujie Zheng
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paper_authors.fullname: Chujie Zheng
paper_authors.type: user
paper_authors.user: chujiezheng
paper_authors._id: 674e6cb0288854089119b0c8
paper_authors.name: Daniel Cooper
paper_authors._id: 674e6cb0288854089119b0c9
paper_authors.name: Ethan Dai
paper_authors._id: 674e6cb0288854089119b0ca
paper_authors.name: Fan Zhou
paper_authors._id: 674e6cb0288854089119b0cb
paper_authors.name: Feng Hu
paper_authors._id: 674e6cb0288854089119b0cc
paper_authors.name: Heng Ji
paper_authors._id: 674e6cb0288854089119b0cd
paper_authors.name: Howard Qiu
paper_authors._id: 674e6cb0288854089119b0ce
paper_authors.name: Jiangcheng Zhu
paper_authors._id: 674e6cb0288854089119b0cf
paper_authors.name: Jun Tian
paper_authors._id: 674e6cb0288854089119b0d0
paper_authors.name: Katherine Su
paper_authors._id: 674e6cb0288854089119b0d1
paper_authors.name: Lihuan Zhang
paper_authors._id: 674e6cb0288854089119b0d2
paper_authors.name: Liying Li
paper_authors._id: 674e6cb0288854089119b0d3
paper_authors.name: Ming Song
paper_authors._id: 674e6cb0288854089119b0d4
paper_authors.name: Mou Li
paper_authors._id: 674e6cb0288854089119b0d5
paper_authors.name: Peng Liu
paper_authors._id: 674e6cb0288854089119b0d6
paper_authors.name: Qichen Hu
paper_authors._id: 674e6cb0288854089119b0d7
paper_authors.name: Shawn Wang
paper_authors._id: 674e6cb0288854089119b0d8
paper_authors.name: Shijun Zhou
paper_authors._id: 674e6cb0288854089119b0d9
paper_authors.name: Shiyong Li
paper_authors._id: 674e6cb0288854089119b0da
paper_authors.name: Tianhang Zhu
paper_authors._id: 674e6cb0288854089119b0db
paper_authors.name: Wen Xie
paper_authors._id: 674e6cb0288854089119b0dc
paper_authors.name: Xiang He
paper_authors._id: 674e6cb0288854089119b0dd
paper_authors.name: Xiaobo Chen
paper_authors._id: 674e6cb0288854089119b0de
paper_authors.name: Xiaohui Hu
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paper_authors.name: Xiaoyi Ren
paper_authors._id: 674e6cb0288854089119b0e0
paper_authors.name: Xinyao Niu
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paper_authors.fullname: Xinyao Niu
paper_authors.type: user
paper_authors.user: sirius-ctrl
paper_authors._id: 674e6cb0288854089119b0e1
paper_authors.name: Yanpeng Li
paper_authors._id: 674e6cb0288854089119b0e2
paper_authors.name: Yongke Zhao
paper_authors._id: 674e6cb0288854089119b0e3
paper_authors.name: Yongzhen Luo
paper_authors._id: 674e6cb0288854089119b0e4
paper_authors.name: Yuchi Xu
paper_authors._id: 674e6cb0288854089119b0e5
paper_authors.name: Yuxuan Sha
paper_authors._id: 674e6cb0288854089119b0e6
paper_authors.name: Zhaodong Yan
paper_authors._id: 674e6cb0288854089119b0e7
paper_authors.name: Zhiyuan Liu
paper_authors._id: 674e6cb0288854089119b0e8
paper_authors.name: Zirui Zhang
paper_title: Yi-Lightning Technical Report
paper_summary: This technical report presents Yi-Lightning, our latest flagship large
  language model (LLM). It achieves exceptional performance, ranking 6th overall
  on Chatbot Arena, with particularly strong results (2nd to 4th place) in
  specialized categories including Chinese, Math, Coding, and Hard Prompts.
  Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture,
  featuring advanced expert segmentation and routing mechanisms coupled with
  optimized KV-caching techniques. Our development process encompasses
  comprehensive pre-training, supervised fine-tuning (SFT), and reinforcement
  learning from human feedback (RLHF), where we devise deliberate strategies for
  multi-stage training, synthetic data construction, and reward modeling.
  Furthermore, we implement RAISE (Responsible AI Safety Engine), a
  four-component framework to address safety issues across pre-training,
  post-training, and serving phases. Empowered by our scalable super-computing
  infrastructure, all these innovations substantially reduce training, deployment
  and inference costs while maintaining high-performance standards. With further
  evaluations on public academic benchmarks, Yi-Lightning demonstrates
  competitive performance against top-tier LLMs, while we observe a notable
  disparity between traditional, static benchmark results and real-world, dynamic
  human preferences. This observation prompts a critical reassessment of
  conventional benchmarks' utility in guiding the development of more intelligent
  and powerful AI systems for practical applications. Yi-Lightning is now
  available through our developer platform at https://platform.lingyiwanwu.com.
paper_ai_summary: Yi-Lightning, a large language model with enhanced Mixture-of-Experts architecture and RAISE safety framework, achieves top-tier performance in specialized categories while being cost-effective and addressing safety issues.
paper_ai_keywords: large language model (LLM) Mixture-of-Experts (MoE) expert segmentation routing mechanisms KV-caching techniques supervised fine-tuning (SFT) reinforcement learning from human feedback (RLHF) RAISE (Responsible AI Safety Engine)