mlsanityData to 7 Oct 2026

Yi-Lightning Technical Report

arXiv 2412.01253 ↗First posted 2 Dec 2024cs.CL cs.AI cs.LGcited by 11

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

also in 1 more
Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 45 of its 55 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

45 of 55 references.

References · 45

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  5. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
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  11. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  12. 2023Let's Verify Step by Stepcited by 3,598
33 more
  1. 2021Program Synthesis with Large Language Modelscited by 3,509
  2. 2023Qwen Technical Reportcited by 3,273
  3. 2023Mistral 7Bcited by 3,065
  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
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  19. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraintcited by 346
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  21. 2024HelpSteer2: Open-source dataset for training top-performing reward modelscited by 232
  22. 2024WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wildcited by 211
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  25. 2024On Prompt-Driven Safeguarding for Large Language Modelscited by 109
  26. 2023In-context Pretraining: Language Modeling Beyond Document Boundariescited by 76
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 1.

11 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2023YAYI 2: Multilingual Open-Source Large Language Models
  2. 2024OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
  3. 2026JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
  4. 2025Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
  5. 2025Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models
  6. 2025Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics
  7. 2023An Empirical Study of Instruction-tuning Large Language Models in Chinese
  8. 2024AI Safety in Generative AI Large Language Models: A Survey