Title: Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Vision-Language Models

Spatial Reasoning
Abstract: As textual reasoning with large language models (LLMs) has advanced significant, there has been growing interest in enhancing the multimodal reasoning capabilities of large vision-language models (LVLMs). However, existing methods primarily approach multimodal reasoning in a straightforward, text-centric manner, where both reasoning and answer derivation are conducted purely through text, with the only difference being the presence of multimodal input. As a result, these methods often encounter fundamental limitations in spatial reasoning tasks that demand precise geometric understanding and continuous spatial tracking\textemdash capabilities that humans achieve through mental visualization and manipulation. To address the limitations, we propose drawing to reason in space, a novel paradigm that enables LVLMs to reason through elementary drawing operations in the visual space. By equipping models with basic drawing operations including annotating bounding boxes and drawing auxiliary lines, we empower them to express and analyze spatial relationships through direct visual manipulation, meanwhile avoiding the performance ceiling imposed by specialized perception tools in previous tool-integrated reasoning approaches. To cultivate this capability, we develop a three-stage training framework: cold-start training with synthetic data to establish basic drawing abilities, reflective rejection sampling to enhance self-reflection behaviors, and reinforcement learning to directly optimize for target rewards. Extensive experiments demonstrate that our model, named \textsc{Spark}, consistently outperforms existing methods across diverse spatial reasoning benchmarks involving maze navigation, static spatial reasoning, video-based reasoning and multi-view-based reasoning tasks, with an average improvement of 11.5\%. Ablation studies reveal the critical role of each training stage, with reflective rejection sampling particularly enhancing the model's self-correction capabilities and reasoning potential.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new model called SPARK, leveraging ‘drawing to reason in space’, a vision centric alternative to the tex-only chain of thought that current large vision-language models (LVLMs) use. SPARK is a VLM (built on a Qwen-2.5-VL-7B backbone) that combine natural language thinking with 2 explicit visual operations: i) drawing bonding boxes and ii) drawing auxiliary lines. With the 2 additional mechanisms, SPARK can externalize spatial hypothesis directly on the image (or video frames). SPARK is trained in 3 steps: i) Cold start supervised fine-tuning (to teach basic drawing skills), ii) reflective rejection sampling and iii) a Reinforcement Learning training. SPARK is evaluated in 5 benchmarks covering static images, videos and multi-view scenes. SPARKS consistently outperforms open-source LVLMs (up to 72B) and specialized reasoning systems.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: The Vision-centred reasoning paradigm is novel and well motivated: by allowing the model to draw spatial artifacts, the model aligned more closely with human spatial cognition. The 3 stages curriculum is well motivated and also empirically validated (thrgouht ablation studies). The benchamarks are broad enough to convincingly demonstrate the performance of SPARK. I have appreciated the interpretable outputs: the drawn artifacts make the model’s reasoning chain verifiable by humans, which is a useful property for safety and debugging.\n\nWeaknesses: \n1. Only boxes and straight lines are supported; many spatial tasks (3D rotation, occlusion…) might need richer geometric primitives to be solved.\n2. The training and the evaluation avoid free-form answers; realisms and linguistic generalization remain untested.\n3. Concerning the reward design, it seems that the same correctness/MRA formula drives both the optimisation and the test evaluation… I am concerned because there is a huge risk of overfitting…\n4. There is not quantification of the computation overhead: drawing and image re-encoding each step adds inference latency and memory, would be good to at least quantify what it the additional compute/overhead.\n\nQUESTIONS:\n1. Do you think it would be possible to extend the operation set (e.g. drawing polygone, heat maps) without retraining from scratch ? If so, please discuss and what kind of tasks you expect improvement\n2. There is no systematic study on how SPARK is sensitive to video length (or image resolution) ? Does it scale well to long video sequence (or very high resolution images)\n3. Have you tried different backbone (e.g. larger backbone like Qwen 72B). Do you think SPARK could easily scale up ? And also do you think it will work with vision encoder (e.g. CLIP-VIT)\n4. What is the average wall-clock latency per chain-of-thought compared with classical text-only models ? \n5. Does reflective rejection hurt exploration by discarding partially correct, but creative trajectories ?\n\nLIMITATIONS:\nThe main limitation lies in the fact that evolution is restricted to categorical (and numerical) QA. There is no assessment of free-form explanations or open-ended planning tasks.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SPARK to enhance the spatial reasoning capabilities of LVLMs. Moving beyond the direct application of text-based reasoning to visual tasks, SPARK proposes a \"drawing to reason in space\" paradigm. By employing tools in a manner analogous to LLM agents, SPARK generates visual and textual reasoning paths. The progressive three-stage training framework—comprising cold-start training, reflective rejection sampling, and reinforcement learning—enables substantial performance improvements across multiple spatial reasoning benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- S1. Paradigm Innovation: The paper's \"drawing to reason in space\" paradigm innovates upon previous methods that apply text-centric reasoning to visual tasks by empowering the model to reason through direct drawing operations in the visual space.\n- S2. High Interpretability: The method generates a \"visual chain of thought,\" presenting the reasoning process as a transparent sequence of interleaved images and text. This significantly enhances interpretability and trustworthiness.\n\nWeaknesses\n- W1. Limited Technical Novelty of Training Framework: The proposed three-stage training framework is primarily a sequential application of existing, widely-used methods (cool-start and RL) and may lack sufficient novelty to be a core contribution.\n- W2. Confounding Factors in Performance Gains: It is difficult to attribute the model's strong performance solely to the \"drawing to reason\" paradigm, as it benefits from specialized fine-tuning data that most baseline models did not have access to.\n- W3. Lack of Comparisoin on Computational Cost: The method's high iterative cost creates an unfair comparison against the single-pass baseline models. The experiments lack a control group under an equivalent computational budget (e.g., allowing for multi-sample voting), which makes it difficult to assess the intrinsic effectiveness of the paradigm itself.\n\nQUESTIONS:\n- Q1. The Reasoning Process: Are the reasoning steps generated autonomously by the model? If so, what termination conditions are in place to prevent infinite or non-productive reasoning loops?\n- Q2. The Attribution of Performance Gains: Given that SPARK was fine-tuned on specialized data while the baselines were not, how can the performance gains be attributed specifically to the \"drawing to reason\" paradigm versus the data advantage? Can the ablation study be used to help disentangle these two factors?\n- Q3. The Cost-Benefit of the Iterative Method: Considering the high computational cost of SPARK's iterative process, what are its specific advantages over simpler strategies that also use a larger compute budget (e.g., textual self-correction, sample-and-vote)? What justifies the added complexity of the visual iteration mechanism?\n- Q4. Code and Data Reproducibility: The paper's checklist states that code and data are in the supplementary material, but they could not be located. Could the authors please clarify the access method to ensure reproducibility?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the SPARK framework, a vision language model that can draw to reason in space. The proposed SPARK has basic drawing operations like annotating bounding boxes and drawing auxiliary lines. Trained with a three-stage paradigm: cold start, reflective rejection sampling, and GRPO-like RL, the model can acquire capabilties to perform spatial reasoning tasks.\n\nEvaluated on multiple spatial reasoning benchmarks spanning maze navigation, static spatial reasoning, video-based temporal tracking, and multi-view scene understanding, the model achieved good performance compared to base VL models.\n\nSTRENGTHS AND WEAKNESSES:\n#### Strengths:\n\n- The paper is well-written, and the idea of drawing facilitates visual reasoning is intriguing. This paper successfully makes some good attempts at letting large language models draw auxiliary lines in visual space to help their reasoning. \n\n- The three-stage paradigm is up-to-date with contemporary advances in RL for LLM reasoning. It's good to see that it can be scaled to more operators.\n\n#### Weaknesses:\n\n- The drawing concept is a bit bluffing. Here, the drawing defined in this paper is merely bounding boxes and simple lines -- which have limited usage cases beyond simple spatial reasoning.\n\n- Lacking baseline comparisons for spatial reasoning involving tool use: how does this compare to visual programming-like approaches for solving spatial relationship tasks [A, B]?\n\n- Ablations: It seems that stage 1 is the most important stage. I suspect that the first step is more like some fine-tuning on the spatial reasoning tasks, making it unfair to compare to other base VL models. What if you only perform query-answer (as in standard VQA) fine-tuning? Does the drawing really improve the performance?\n\nref:\n\n[A]. Gupta, T., & Kembhavi, A. (2023). Visual programming: Compositional visual reasoning without training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14953-14962).\n\n[B]. Surís, D., Menon, S., & Vondrick, C. (2023). Vipergpt: Visual inference via python execution for reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 11888-11898).\n\nQUESTIONS:\nre weaknesses: What if you only perform query-answer (as in standard VQA) fine-tuning? Does the drawing really improve the performance? How does this compare to visual programming-like approaches for solving spatial relationship tasks [A, B]?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
The paper introduces SPARK, a LVLM that doing multimodal reasoning via interleaved text reasoning and visual drawing. There are two drawing operations, drawing bounding boxes and auxiliary lines, on images and videos. The training recipe includes three stages: (1) cold-start SFT (2) reflective rejection sampling (3) RL. The model, which is fine-tuned over Qwen-2.5 VL, outperform the original model by an average of 11.5%. Ablation shows the effectiveness of each approach.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The motivation is clear, and the method is reasonable.
2. The approach effectively gives LVLM the ability to reason with interleaved reasoning trajectories.
3. Extensive experiments on diverse image and video tasks.

Weaknesses:
1. More analysis on the inference-time scaling would be helpful. The author only shows the difference of pass@1 and pass@8. However, a more interesting dimension is the number of tokens the model uses to answer the question. It would be great to include, for example, the number of tokens and the number of tool calls during the process of reinforcement learning.
2. More analysis on the effectiveness of each training stage would be useful. What if we remove one of the stages? How would that affect the final performance?

QUESTIONS:
1. The model is getting big improvements on MAZE, but relatively small gains on some other tasks. Can you give more insights on that? What new tools/training strategies do we need to make the approach more effective on these other tasks?

2. As I mentioned in the weaknesses, it would be good to include ablation on each training stage

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03