Yi-Lightning Technical Report
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
- 2023GPT-4 Technical Reportcited by 18,132
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
- 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
- 2023Let's Verify Step by Stepcited by 3,598
33 more
- 2021Program Synthesis with Large Language Modelscited by 3,509
- 2023Qwen Technical Reportcited by 3,273
- 2023Mistral 7Bcited by 3,065
- 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
- 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
- 2024Qwen2 Technical Reportcited by 2,076
- 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
- 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
- 2024Mixtral of Expertscited by 1,785
- 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,565
- 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,154
- 2023Instruction-Following Evaluation for Large Language Modelscited by 1,143
- 2024DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Modelcited by 1,093
- 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 834
- 2024Yi: Open Foundation Models by 01.AIcited by 706
- 2024MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategiescited by 694
- 2024From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipelinecited by 484
- 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraintcited by 346
- 2023Effective Long-Context Scaling of Foundation Modelscited by 301
- 2024HelpSteer2: Open-source dataset for training top-performing reward modelscited by 232
- 2024WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wildcited by 211
- 2024Data Engineering for Scaling Language Models to 128K Contextcited by 183
- 2024Simple and Scalable Strategies to Continually Pre-train Large Language Modelscited by 122
- 2024On Prompt-Driven Safeguarding for Large Language Modelscited by 109
- 2023In-context Pretraining: Language Modeling Beyond Document Boundariescited by 76
- 2023AlignBench: Benchmarking Chinese Alignment of Large Language Modelscited by 68
- 2024ReLU2 Wins: Discovering Efficient Activation Functions for Sparse LLMscited by 66
- 2023Click: Controllable Text Generation with Sequence Likelihood Contrastive Learningcited by 41
- 2024Model Extrapolation Expedites Alignmentcited by 37
- 2024DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraintscited by 10
- 2024Exploring the Benefit of Activation Sparsity in Pre-trainingcited by 5
- 2024Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimizationcited by 4
Built on by
Citing papers per year: 2024: 1; 2025: 9; 2026: 1.
11 papers.
- 2024ProcessBench: Identifying Process Errors in Mathematical Reasoningcited by 277
- 2025OctoThinker: Mid-training Incentivizes Reinforcement Learning Scalingcited by 110
- 2025OS Agents: A Survey on MLLM-based Agents for General Computing Devices Usecited by 35
- 2025Mid-Training of Large Language Models: A Surveycited by 18
- 2025Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?cited by 15
- 2025Breaking the Data Barrier -- Building GUI Agents Through Task Generalizationcited by 13
- 2025Reinforcement Mid-Trainingcited by 6
- 2025Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspectivecited by 1
- 2025Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challengecited by 0
- 2025Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMscited by 0
- 2026Level Up: Defining and Exploiting Transitional Problems for Curriculum Learningcited by 0
- 2026Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning14 Mar 2026
- 2025Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective9 Oct 2025
- 2025Mid-Training of Large Language Models: A Survey8 Oct 2025
- 2025Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs29 Sep 2025
- 2025Reinforcement Mid-Training29 Sep 2025
- 2025OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use6 Aug 2025
- 2025OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling25 Jun 2025
- 2025Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge14 Jun 2025
- 2025Breaking the Data Barrier -- Building GUI Agents Through Task Generalization14 Apr 2025
- 2025Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?1 Apr 2025
- 2024ProcessBench: Identifying Process Errors in Mathematical Reasoning9 Dec 2024
Similar, not cited
Close in wording, no citation either way
- 2023YAYI 2: Multilingual Open-Source Large Language Models
- 2024OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
- 2026JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
- 2025Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
- 2025Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models
- 2025Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics
- 2023An Empirical Study of Instruction-tuning Large Language Models in Chinese
- 2024AI Safety in Generative AI Large Language Models: A Survey