mlsanityData to 7 Oct 2026

Limited Linguistic Diversity in Embodied AI Datasets

arXiv 2601.03136 ↗First posted 6 Jan 2026cs.CL cs.AI cs.ROcited by 5

On the map: Vision-language-action models → Generalist VLA models and benchmarks

Abstract (arXiv)

Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate these systems remain poorly documented. In this work, we present a systematic dataset audit of several widely used VLA corpora, aiming to characterize what kinds of instructions these datasets actually contain and how much linguistic variety they provide. We quantify instruction language along complementary dimensions--including lexical variety, duplication and overlap, semantic similarity, and syntactic complexity. Our analysis shows that many datasets rely on highly repetitive, template-like commands with limited structural variation, yielding a narrow distribution of instruction forms. We position these findings as descriptive documentation of the language signal available in current VLA training and evaluation data, intended to support more detailed dataset reporting, more principled dataset selection, and targeted curation or augmentation strategies that broaden language coverage.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Generalist VLA models and benchmarks, one of 13 directions in Vision-language-action models. Builds on 39 of its 69 references that are in the map. First posted on arXiv 6 Jan 2026.

Builds on

39 of 69 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better29 May
  2. 2025π0.5: a Vision-Language-Action Model with Open-World Generalization22 Apr
  3. 2024LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation7 Oct
  4. 2024OpenVLA: An Open-Source Vision-Language-Action Model13 Jun

Other references · 35

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  5. 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,007
  6. 2016Understanding deep learning requires rethinking generalizationcited by 1,657
  7. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
  8. 2023LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learningcited by 1,338
  9. 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
  10. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  11. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  12. 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 634
23 more
  1. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 484
  2. 2020The Pitfalls of Simplicity Bias in Neural Networkscited by 349
  3. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
  4. 2021Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasetscited by 248
  5. 2022Interactive Language: Talking to Robots in Real Timecited by 217
  6. 2020Evaluating the Evaluation of Diversity in Natural Language Generationcited by 127
  7. 2025ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Modelcited by 112
  8. 2023SONAR: Sentence-Level Multimodal and Language-Agnostic Representationscited by 105
  9. 2024Why are Visually-Grounded Language Models Bad at Image Classification?cited by 101
  10. 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 97
  11. 2024No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performancecited by 75
  12. 2020Diagnosing the Environment Bias in Vision-and-Language Navigationcited by 41
  13. 2022An Analysis of Negation in Natural Language Understanding Corporacited by 39
  14. 2025Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentationcited by 31
  15. 2022Semantic Diversity in Dialogue with Natural Language Inferencecited by 28
  16. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  17. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  18. 2025DataMIL: Selecting Data for Robot Imitation Learning with Datamodelscited by 25
  19. 2025NegVQA: Can Vision Language Models Understand Negation?cited by 12
  20. 2022Towards Robust NLG Bias Evaluation with Syntactically-diverse Promptscited by 7
  21. 2023How Well Do Text Embedding Models Understand Syntax?cited by 6
  22. 2024Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?cited by 5
  23. 2024SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpuscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Controlcited by 19
  2. 2026TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformerscited by 14
  3. 2026LangGap: Diagnosing and Closing the Language Gap in Vision-Language-Action Modelscited by 4
  4. 2026VLA Grounder: Language-Conditioning Space Optimization for Black-Box VLA Modelscited by 1
  5. 2026RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines
  2. 2026EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
  3. 2025Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models
  4. 2026Beyond English: Uncovering the Multilingual Gap in Vision-Language-Action Models
  5. 2025CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models
  6. 2025Unveiling the Potential of Vision-Language-Action Models with Open-Ended Multimodal Instructions
  7. 2024A Survey on Vision-Language-Action Models for Embodied AI
  8. 2025IA-VLA: Input Augmentation for Vision-Language-Action models in settings with semantically complex tasks