Title: VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Venue: NeurIPS 2025
Accepted: yes
Keywords: MLLM

Test Time Scaling
Abstract: Inducing reasoning in multimodal large language models (MLLMs) is critical for achieving human-level perception and understanding. Existing methods mainly leverage LLM reasoning to analyze parsed visuals, often limited by static perception stages. This paper introduces Visual Test-Time Scaling (VTTS), a novel approach to enhance MLLMs' reasoning via iterative perception during inference. VTTS mimics humans' hierarchical attention by progressively refining focus on high-confidence spatio-temporal regions, guided by updated textual predictions. Specifically, VTTS employs an Iterative Perception (ITP) mechanism, incorporating reinforcement learning with spatio-temporal supervision to optimize reasoning. To support this paradigm, we also present VTTS-80K, a dataset tailored for iterative perception.
These designs allows a MLLM to enhance its performance by increasing its perceptual compute.  Extensive experiments validate VTTS's effectiveness and generalization across diverse tasks and benchmarks. Our newly introduced Videochat-R1.5 model has achieved remarkable improvements, with an average increase of over 5\%, compared to robust baselines such as Qwen2.5VL-3B and -7B, across more than 15 benchmarks that encompass video conversation, video reasoning, and spatio-temporal perception.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nVTTS lets an MLLM re-perceive its visual input several times during inference: after each reasoning step the model predicts a region of interest, crops/resamples that patch, and feeds it back through the vision encoder, with a reinforcement-learned policy (trained via GRPO on the new VTTS-80K dataset) guiding which patches to attend to. Added to Qwen 2.5-VL (3 B and 7 B) this iterative perception loop yields 5–6% average accuracy gains on more than twenty image- and video-reasoning benchmarks, with three iterations giving the best compute-accuracy trade-off.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n- S1: The work tackles the significant challenge of scaling multimodal reasoning in MLLMs via iterative, adaptive computation, grounded in cognitive inspiration (humans' hierarchical attention).\n\n- S2: The core proposal of VTTS with ITP and reinforcement learning is a meaningful extension beyond static perception, modeling perceptual dependencies through reinforcement signals tied to spatio-temporal cues.\n\n- S3: VTTS-80K is a well-motivated resource, with multi-granular annotations (QA, spatio-temporal support, stepwise CoT), systematically constructed as seen in Figure 3, supporting both training and benchmarking of iterative visual reasoning.\n\n- S4: The evaluation covers a wide variety of tasks (video, image, spatial, temporal) and provides quantitative evidence for improvements.\n\n### Weaknesses\n\n- W1:  While the model is compared to strong open-source and some proprietary baselines, some result tables (e.g., Table 7 for spatial grounding) hint that specialized detection and grounding models (e.g., Grounding-DINO-L) are stronger in certain settings. The discussion of tradeoffs versus domain-specific approaches could be more critical—are there settings where VTTS-based models are still weaker?\n\n- W2: The computation and efficiency analysis are missing, such as inference-time FLOPs or latency. Iterative perception may be costly, especially on long videos.\n\n- W3: Limited backbone diversity. Results are only on Qwen-VL 3 B/7 B. Claims of generality would be stronger with models that use different vision encoders.\n\n- W4: No head-to-head with other vision-side TTS baselines such as Visual-CoT or V*, thus the gains cannot be cleanly attributed to the new components. Another straightforward training-free baseline (or ablation study) is sampling $k$ random spatial (or temporal) crops from the input image/video, and fusing the answers by a simple majority vote.\n\n- W5: Inconsistent tense and plurality could be improved for better clarity and professional polish.\n\nQUESTIONS:\n- Could the authors elaborate, possibly with a formal discussion or empirical study, why reinforcement learning as implemented here succeeds where supervised fine-tuning does not in narrowing down regions of interest? Is there a formal intuition or guarantee about convergence or optimality?\n\n- What are the practical computation costs (e.g., latency, GPU-hours) associated with 3+ iterations of ITP at inference? Is there a diminishing returns point, and how should practitioners select the right trade-off for deployment?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces VTTS (Visual Test-Time Scaling), a method to enhance reasoning in Multimodal Large Language Models (MLLMs) via iterative visual perception at inference time. Inspired by human-like progressive attention, VTTS refines attention to high-confidence spatio-temporal regions in multiple rounds, driven by evolving textual predictions. It proposes: 1) An Iterative Perception (ITP) mechanism using Reinforcement Learning (RL) based on GRPO. 2) A new dataset, VTTS-80K, designed for training MLLMs with spatio-temporal grounding and reasoning via RL. 3) Extensive experiments showing 5–6% performance boosts over strong MLLM baselines across more than 20 benchmarks, including video QA, image reasoning, and perception.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**\n\n* VTTS introduces a principled way to scale perceptual computation at test time, rather than only scaling generation or language reasoning. \n* Demonstrates consistent improvements (1–6% gains) across a wide range of tasks and benchmarks using Qwen2.5-VL models.\n* New Benchmark Dataset: VTTS-80K provides high-quality, multi-level annotations (QA, spatio-temporal regions, and CoT) for robust training.\n* Detailed Ablation Studies: Includes insightful ablations showing impact of perception steps, data components, and training methods.\n\n---\n\n### **Weaknesses**\n\n* Disentangling Visual Scaling from Language Scaling: Given that language reasoning can also benefit from more decoding steps (e.g., Best-of-N, beam search), how do the authors ensure that the performance gains from VTTS are due to visual test-time scaling rather than indirectly benefiting from repeated language generation?\n* The paper models the ROI selection process as a reinforcement learning problem using GRPO. Could the authors elaborate on the nature of the learned spatio-temporal policy? Is the policy generalizable across tasks, or is it overfit to the specific structure of VTTS-80K?\n* Engineering cost at inference is high: Iterative perception increases test-time compute, which might be impractical for real-time applications without optimization.\n* Quality of VTTS-80K may be bottlenecked by the capabilities of DeepSeek or other LLMs used in the annotation process. It is a concern that dataset generation pipeline heavily relies on LLMs.\n\nQUESTIONS:\n* Heavy Reliance on Qwen2.5-VL: (no worries if this may take some time and cannot be completed during rebuttal, I can understand it.) All model improvements are tied to Qwen2.5; generalization to other MLLMs (e.g., LLaVA, Gemini) may need to be demonstrated. Could authors have a try on other frameworks if possible? \n\nI appreciate the completeness of this system paper, which demonstrates a well-integrated effort across dataset construction, model architecture design, and comprehensive empirical evaluation. However, I have concerns regarding the generalizability of the proposed algorithm and would appreciate further clarification on the disentanglement between visual scaling and language-level scaling effects.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Visual Test-Time Scaling (VTTS), a method that enhances MLLM reasoning by enabling iterative visual perception during inference. Mimicking human attention, VTTS progressively refines focus on key spatio-temporal regions guided by evolving text predictions. It introduces an Iterative Perception (ITP) mechanism, trained via Generalized Reward Policy Optimization (GRPO) with spatio-temporal supervision. This paper also presents VTTS-80K, a dataset tailored for this paradigm. VTTS achieves consistent improvements across 20+ benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. VTTS mimics human hierarchical attention by iteratively refining focus on high-confidence spatio-temporal regions. This idea makes sense in many scenarios.\n2. Extensive and detailed experiments validate the effectiveness of the proposed method across a wide range of tasks, including Multimodal Video Understanding, Multimodal Image Understanding, Grounded Video Question Answering, Temporal Grounding, Spatial Grounding, and Object Tracking.\n\nWeaknesses:\n\nMajor Weakness\n1. The paper lacks a direct comparison with other state-of-the-art methods built upon the Qwen2.5-VL or other models.\n2. In terms of reinforcement learning, only the reward function was modified, with no significant novelty.\n3. Compared to the increased inference time of this method, the performance improvement is not significant enough.\n4. Although the idea that focuses on the temporal clue and spatial clue makes sense, the performance improvements of Table 1 (video benchmarks) and Table 2 (image benchmarks) are not satisfactory. Why is this the case? Is there any detailed analysis?\n\nMinor weakness\n1. Mistakenly wrote \"Fine\" as \"Fune\" in table 5.\n2.  Mistakenly wrote \"Clue\" as \"Glue\" in figure 3.\n3. The text in the figures is a bit blurry.\n\nQUESTIONS:\n1. What would the performance look like if we directly use ground-truth clues for video or image QA? Alternatively, how does it compare to first extracting clues using a state-of-the-art model, followed by perception? How do these non-training strategies differ from trained approaches in terms of both accuracy and computational efficiency?\n2. Analyze why Supervised Fine-Tuning (SFT) may degrade performance in some cases, while GRPO can lead to improvements.\n3. The method performs poorly when using only the first perception step and requires multiple iterations to achieve good performance. Why is this the case?\n4. It takes more time to perceive temporal and spatial clues multiple times. Did the author count the time taken in different situations? How much difference is there in time efficiency between it and the SFT model (directly outputs the answer) and the inference model (single-inference text output)?\n5. Did the model undergo the Cold Start before reinforcement training? Did the author do some detailed ablation experiments to show whether there was any impact of cold start?\n6. The authors do not provide visualization of the reasoning results of specific instances of the proposed model, including output reasoning paths and visualizations, which makes it difficult to understand how the model performs video or image reasoning.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper introduces Visual Test-Time Scaling (VTTS), a framework that enhances MLLMs through iterative perception during inference. The core innovation lies in mimicking human hierarchical attention by progressively refining focus on high-confidence spatio-temporal regions, guided by evolving textual predictions. The authors also contribute VTTS-80K, a dataset tailored for iterative perception training. Experiments on video and image understanding show significant gains on Qwen-VL baselines.

STRENGTHS AND WEAKNESSES:
Strengths: 
- The proposed concept of iterative perception is reasonable and effective. 
- The integration of reinforcement learning (GRPO) with spatio-temporal supervision is well-designed. 
- The proposed VTTS datasets are valuable for the research community. 

Weaknesses: 
- Lack of some essential comparisons and experiments. Please refer to the question period. 
- The authors include no sota reasoning approaches in the table for comparison.

QUESTIONS:
1. What is the computational overhead of the iterative inference, compared with the direct answer baseline and other long-chain inference methods (e.g. LLaVA-CoT, Mulberry, etc.)?
2. The proposed concept of test-time scaling is a type of reasoning method. What is the performance compared with other reasoning post-training (e.g. LLaVA-CoT, Mulberry, Insight-V, MM-Eureka, Visual-CoT, etc) and test-time scaling approaches?

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