Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning

• URL: http://arxiv.org/abs/2606.11719v1
• Date: Wed, 10 Jun 2026 06:49:21 GMT
• Title: Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning
• Abstract summary: Existing approaches largely rely on large-scale, statically curated datasets.<n>Ouroboros-Spatial is a self-evolving training framework in which the model plays dual roles as a proposer and a solver.<n>Ouroboros-Spatial substantially improves Qwen3-VL-4B and Qwen3-VL-8B while using an order of fewer training examples than recent large-scale curated datasets.
• Score: 13.767813688729161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs). Existing approaches largely rely on large-scale, statically curated datasets, where all training samples are treated uniformly regardless of the model's evolving capabilities. This static paradigm is inherently data-inefficient: training capacity is often spent on samples that are either trivial or overly difficult for the model at its current stage. To address this limitation, we propose Ouroboros-Spatial, a self-evolving training framework in which the model plays dual roles as a proposer and a solver. In each iteration, a frozen proposer generates spatial question-answer (QA) pairs from 3D scene metadata and raw video frames, together with executable code for deriving reliable ground truth. A learnable solver is then fine-tuned on the accepted samples, and its per-sample prediction confidence is used as a difficulty signal. This signal is fed back to the proposer in the next iteration, guiding it to generate questions better matched to the solver's current capabilities. Through this closed-loop design, the training distribution co-evolves with model ability, reducing redundant trivial examples while filtering out ambiguous or uninformative samples with limited learning value. Across six spatial reasoning benchmarks, Ouroboros-Spatial substantially improves Qwen3-VL-4B and Qwen3-VL-8B while using an order of magnitude fewer training examples than recent large-scale curated datasets. On VSI-Bench, it yields absolute gains of 9.9 and 6.8 points for the 4B and 8B models, respectively, enabling both to outperform a wide range of strong open-source and proprietary baselines.

Related papers

• Learning to Solve Generative ODEs Beyond the Linear Span [50.13853710831612]
  We propose SpanLift, a lightweight neural solver that augments scalar-coefficient updates with a spatial residual operator.<n>Across pixel-space diffusion, latent flow matching, and precipitation nowcasting, SpanLift achieves state-of-the-art few-step sampling.
  arXiv  Detail & Related papers  (2026-06-07T15:22:13Z)
• Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis [37.03537014865641]
  We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the environments that train it.<n>We instantiate this view in EvoEnv, a single-policy generator, solver method that synthesizes Python environments from ten seeds.
  arXiv  Detail & Related papers  (2026-05-14T05:14:45Z)
• Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching [66.39914384073145]
  We propose a self-consistency framework that turns cheap diffusion-sampled reasoning into a reusable pool of step-level candidates.<n>We find that step-level recombination is most beneficial on harder problems.<n>Our training-free framework improves average accuracy by up to 2 across six math and coding tasks.
  arXiv  Detail & Related papers  (2026-02-26T11:08:39Z)
• VADE: Variance-Aware Dynamic Sampling via Online Sample-Level Difficulty Estimation for Multimodal RL [38.782188833641676]
  Group-based policy optimization methods like GRPO and GSPO have become standard for training multimodal models.<n>They suffer from a critical emphgradient vanishing problem when all responses within a group receive identical rewards.<n>We propose textbfVADE, a sampling framework via online sample-level difficulty textbfEstimation.
  arXiv  Detail & Related papers  (2025-11-24T08:59:54Z)
• Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering [51.7496756448709]
  Language models (LMs) perform well on coding benchmarks but struggle with real-world software engineering tasks.<n>Existing approaches rely on supervised fine-tuning with high-quality data, which is expensive to curate at scale.<n>We propose Test-Time Scaling (EvoScale), a sample-efficient method that treats generation as an evolutionary process.
  arXiv  Detail & Related papers  (2025-05-29T16:15:36Z)
• Fourier Head: Helping Large Language Models Learn Complex Probability Distributions [7.074506869260538]
  We introduce a neural network layer, constructed using Fourier series, which we can easily substitute for any linear layer if we want the outputs to have a more continuous structure.<n>We perform extensive analysis on synthetic datasets, as well as on large-scale decision making and time series forecasting tasks.<n>All of our results support the effectiveness of our proposed Fourier head in scenarios where the underlying data distribution has a natural continuous structure.
  arXiv  Detail & Related papers  (2024-10-29T17:27:58Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• DiffComplete: Diffusion-based Generative 3D Shape Completion [114.43353365917015]
  We introduce a new diffusion-based approach for shape completion on 3D range scans. We strike a balance between realism, multi-modality, and high fidelity. DiffComplete sets a new SOTA performance on two large-scale 3D shape completion benchmarks.
  arXiv  Detail & Related papers  (2023-06-28T16:07:36Z)
• The Right Tool for the Job: Matching Model and Instance Complexities [62.95183777679024]
  As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. We propose a modification to contextual representation fine-tuning which, during inference, allows for an early (and fast) "exit" We test our proposed modification on five different datasets in two tasks: three text classification datasets and two natural language inference benchmarks.
  arXiv  Detail & Related papers  (2020-04-16T04:28:08Z)
• Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension [27.538957000237176]
  Humans create questions adversarially, such that the model fails to answer them correctly. We collect 36,000 samples with progressively stronger models in the annotation loop. We find that training on adversarially collected samples leads to strong generalisation to non-adversarially collected datasets. We find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop.
  arXiv  Detail & Related papers  (2020-02-02T00:22:55Z)

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.