mlsanityData to 14 Oct 2026

Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models

arXiv 2404.12387 ↗First posted 18 Apr 2024cs.CL cs.CVcited by 67

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images, video, and audio inputs. This technical report discusses details of training some of these models and provides comprehensive evaluation results. We show that Reka Edge and Reka Flash are not only state-of-the-art but also outperform many much larger models, delivering outsized values for their respective compute class. Meanwhile, our most capable and largest model, Reka Core, approaches the best frontier models on both automatic evaluations and blind human evaluations. On image question answering benchmarks (e.g. MMMU, VQAv2), Core performs competitively to GPT4-V. Meanwhile, on multimodal chat, Core ranks as the second most preferred model under a blind third-party human evaluation setup, outperforming other models such as Claude 3 Opus. On text benchmarks, Core not only performs competitively to other frontier models on a set of well-established benchmarks (e.g. MMLU, GSM8K) but also outperforms GPT4-0613 on human evaluation. On video question answering (Perception-Test), Core outperforms Gemini Ultra. Models are shipped in production at http://chat.reka.ai . A showcase of non cherry picked qualitative examples can also be found at http://showcase.reka.ai .

Key facts. Cited by 67 papers in the map (data through 14 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 26 of its 32 references that are in the map. First posted on arXiv 18 Apr 2024.

Builds on

26 of 32 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2017Proximal Policy Optimization Algorithmscited by 15,215
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  7. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  8. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  9. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  10. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  11. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  12. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
14 more
  1. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  2. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  3. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,516
  4. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
  5. 2020GLU Variants Improve Transformercited by 1,804
  6. 2019Root Mean Square Layer Normalizationcited by 1,480
  7. 2023GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpointscited by 1,346
  8. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  9. 2019Fast Transformer Decoding: One Write-Head is All You Needcited by 717
  10. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  11. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
  12. 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 333
  13. 2023The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variantscited by 311
  14. 2021Few-shot Learning with Multilingual Language Modelscited by 278

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 42; 2025: 20; 2026: 5.

67 papers.

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  1. 2024Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysiscited by 1,413
  2. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,050
  3. 2024VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMscited by 771
  4. 2024Long Context Transfer from Language to Visioncited by 515
  5. 2024LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Modelscited by 353
  6. 2024OLMoE: Open Mixture-of-Experts Language Modelscited by 301
  7. 2024CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMscited by 234
  8. 2024MANTIS: Interleaved Multi-Image Instruction Tuningcited by 216
  9. 2024MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understandingcited by 182
  10. 2024Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durationscited by 170
  11. 2025WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMscited by 155
  12. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 135
38 more
  1. 2025Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalitiescited by 110
  2. 2024OmniBench: Towards The Future of Universal Omni-Language Modelscited by 102
  3. 2024TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Modelscited by 73
  4. 2024Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Modelscited by 72
  5. 2024Reinforcement Learning Enhanced LLMs: A Surveycited by 71
  6. 2025ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Modelscited by 58
  7. 2024The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audiocited by 53
  8. 2024ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videoscited by 51
  9. 2024AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?cited by 47
  10. 2024Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussionscited by 42
  11. 2024SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domaincited by 39
  12. 2024Prism: A Framework for Decoupling and Assessing the Capabilities of VLMscited by 32
  13. 2024Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arenacited by 26
  14. 2024Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Surveycited by 23
  15. 2025Relational Deep Learning: Challenges, Foundations and Next-Generation Architecturescited by 23
  16. 2024VisionArena: 230K Real World User-VLM Conversations with Preference Labelscited by 22
  17. 2024From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalitiescited by 22
  18. 2024The 2024 Foundation Model Transparency Indexcited by 19
  19. 2025AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMscited by 19
  20. 2024From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesiscited by 17
  21. 2024GAMEBoT: Transparent Assessment of LLM Reasoning in Gamescited by 17
  22. 2024VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Textcited by 15
  23. 2024TuBA: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuningcited by 14
  24. 2024Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptionscited by 13
  25. 2024Needle Threading: Can LLMs Follow Threads through Near-Million-Scale Haystacks?cited by 12
  26. 2024Jailbreaking as a Reward Misspecification Problemcited by 11
  27. 2024Data, Data Everywhere: A Guide for Pretraining Dataset Constructioncited by 11
  28. 2025Aurelia: Test-time Reasoning Distillation in Audio-Visual LLMscited by 9
  29. 2024CL-Attack: Textual Backdoor Attacks via Cross-Lingual Triggerscited by 8
  30. 20254D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understandingcited by 8
  31. 2025VideoAds for Fast-Paced Video Understandingcited by 8
  32. 2025AURA: A Fine-Grained Benchmark and Decomposed Metric for Audio-Visual Reasoningcited by 8
  33. 2026M3-BENCH: Process-Aware Evaluation of LLM Agents' Social Behaviors in Mixed-Motive Gamescited by 8
  34. 2024MixEval-X: Any-to-Any Evaluations from Real-World Data Mixturescited by 6
  35. 2025MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMscited by 6
  36. 2024Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmarkcited by 5
  37. 2026GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinkingcited by 5
  38. 2024Questionable practices in machine learningcited by 4

50 most cited of 67 All 67 (JSON)

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  1. 2024Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models
  2. 2023SEED-Bench-2: Benchmarking Multimodal Large Language Models
  3. 2025LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation
  4. 2024MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
  5. 2024Efficient Multimodal Large Language Models: A Survey
  6. 2023MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
  7. 2024Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions
  8. 2023SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension