Nimbus: A Unified Embodied Synthetic Data Generation Framework

• URL: http://arxiv.org/abs/2601.21449v2
• Date: Mon, 09 Feb 2026 06:57:47 GMT
• Title: Nimbus: A Unified Embodied Synthetic Data Generation Framework
• Abstract summary: Scaling data volume and diversity is critical for generalizing embodied intelligence.<n>We present Nimbus, a unified synthetic data generation framework designed to integrate heterogeneous navigation and manipulation pipelines.<n>Our evaluation demonstrates that Nimbus achieves a 2-3X improvement in end-to-end throughput compared to unoptimized baselines.
• Score: 51.55989844555466
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acquisition, existing pipelines remain fragmented and task-specific. This isolation leads to significant engineering inefficiency and system instability, failing to support the sustained, high-throughput data generation required for foundation model training. To address these challenges, we present Nimbus, a unified synthetic data generation framework designed to integrate heterogeneous navigation and manipulation pipelines. Nimbus introduces a modular four-layer architecture featuring a decoupled execution model that separates trajectory planning, rendering, and storage into asynchronous stages. By implementing dynamic pipeline scheduling, global load balancing, distributed fault tolerance, and backend-specific rendering optimizations, the system maximizes resource utilization across CPU, GPU, and I/O resources. Our evaluation demonstrates that Nimbus achieves a 2-3X improvement in end-to-end throughput compared to unoptimized baselines and ensuring robust, long-term operation in large-scale distributed environments. This framework serves as the production backbone for the InternData suite, enabling seamless cross-domain data synthesis.

Related papers

• DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams [48.434767119224375]
  We propose a paradigm shift towards Agentic Data Tailoring.<n>To overcome the data scarcity bottleneck in training such high-order capabilities, we design a two-stage pipeline.<n>$textDataClaw_0$-9B model synergizes Supervised Fine-Tuning with Group Relative Policy Optimization.
  arXiv  Detail & Related papers  (2026-06-19T11:31:43Z)
• A Real-Calibrated Synthetic-First Data Engine [0.0]
  Real-Calibrated Synthetic-First Data Engine is a modular data engineering framework that combines controllable diffusion generation and multi-stage curation/filtering within a unified pipeline.<n>We show that synthetic data improves a real-data baseline when used as near-zero-human-annotation-cost augmentation alongside real anchors, while synthetic-only training remains substantially below real-only performance.
  arXiv  Detail & Related papers  (2026-05-10T18:34:43Z)
• DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis [30.087790560692643]
  DataArc-SynData-Toolkit is an open-source framework featuring a configuration-driven, end-to-end pipeline equipped with an intuitive visual interface and simplified CLI.<n>By offering an end-to-end and visually interactive pipeline, DataArc-SynData-Toolkit significantly lowers the technical barrier to synthetic data generation and subsequent model training.
  arXiv  Detail & Related papers  (2026-05-02T05:37:58Z)
• Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale [12.736059967085025]
  Modern Deep Learning Recommendation Models (DLRMs) follow scaling laws with sequence length.<n>"Fat Row" paradigm pre-materializes these sequences into every training example, creating a storage and I/O wall.<n>We present a emphversioned late materialization paradigm that eliminates this redundancy by storing UIH once in a normalized, immutable tier.
  arXiv  Detail & Related papers  (2026-04-27T06:41:39Z)
• Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis [68.7701048879757]
  EvoKernel is a self-evolving agentic framework that automates the lifecycle of kernel synthesis.<n>It learns stage-specific Q-values that prioritize experiences based on their contribution to the current objective.<n>It improves models' correctness from 11.0% to 83.0% and a median speedup of 3.60x over initial drafts.
  arXiv  Detail & Related papers  (2026-03-11T14:57:06Z)
• Generative Data Transformation: From Mixed to Unified Data [57.84692191369066]
  textscTaesar is a emphdata-centric framework for textbftarget-textbfal textbfregeneration.<n>It encodes cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures.
  arXiv  Detail & Related papers  (2026-02-26T08:30:09Z)
• TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting [42.2854432715079]
  We present TempoPFN, a time series foundation model based on linear Recurrent Neural Networks (RNNs) pre-trained exclusively on synthetic data.<n>The model uses a GatedDeltaProduct architecture with state-weaving for fully parallelizable training across sequence lengths.
  arXiv  Detail & Related papers  (2025-10-29T13:27:18Z)
• Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data [53.040873127309766]
  We propose a token disentanglement process within the transformer architecture, enhancing feature separation and ensuring more effective learning.<n>Our method outperforms existing models on both in-dataset and cross-dataset evaluations.
  arXiv  Detail & Related papers  (2025-09-08T17:58:06Z)
• SyGra: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data [0.0]
  We present a comprehensive synthetic data generation framework for large language models (LLMs)<n>Our approach employs a modular and configuration-based pipeline capable of modeling complex dialogue flows with minimal manual intervention.<n>The resulting datasets are structured under a flexible schema supporting both SFT and DPO use cases, enabling seamless integration into diverse training.
  arXiv  Detail & Related papers  (2025-08-21T10:35:41Z)
• Scaling Laws of Synthetic Data for Language Models [125.41600201811417]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets.<n>Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models [83.65386456026441]
  Data-Juicer 2.0 is a data processing system backed by 100+ data processing operators spanning text, image, video, and audio modalities.<n>It supports more critical tasks including data analysis, synthesis, annotation, and foundation model post-training.<n>The system is publicly available and has been widely adopted in diverse research fields and real-world products such as Alibaba Cloud PAI.
  arXiv  Detail & Related papers  (2024-12-23T08:29:57Z)
• Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy [2.9005223064604078]
  We introduce a scalable Aerial Synthetic Data Augmentation (ASDA) framework tailored to aerial autonomy applications. ASDA extends a central data collection engine with two scriptable pipelines that automatically perform scene and data augmentations. We demonstrate the effectiveness of our method in automatically generating diverse datasets.
  arXiv  Detail & Related papers  (2022-11-10T04:37:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.