Title: Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multimodal LLM

Visual Reasoning

Cognitive Behavior Transfer
Abstract: The remarkable reasoning capability of large language models (LLMs) stems from cognitive behaviors that emerge through reinforcement with verifiable rewards. This work investigates how to transfer this principle to Multimodal LLMs (MLLMs) to unlock advanced visual reasoning. We introduce a two-stage paradigm built on Qwen2.5-VL-7B: a massive linguistic cold-start fine-tuning, 
followed by multimodal reinforcement learning (RL) spanning nearly 1,000 steps—surpassing all previous open-source efforts in scale.
This pioneering work reveals three fundamental insights: 1) Behavior transfer emerges surprisingly early in cold start due to linguistic mental imagery. 2) Cold start broadly memorizes visual behaviors, while RL critically discerns and scales up effective patterns. 3) Transfer strategically favors high-utility behaviors such as visual reflection. Our resulting model, Open-Vision-Reasoner (OVR), achieves state-of-the-art performance on a suite of reasoning benchmarks, including 95.3% on MATH500, 51.8% on MathVision and 54.6% on MathVerse. We release our model, data, and training dynamics to catalyze the development of more capable, behavior-aligned multimodal reasoners.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors observe that reasoning capabilities can be transferred from language-only to vision-language models, leading to significant gains on visual reasoning tasks. This leads the authors to propose a 3-stage training pipeline for VLMs, encompassing 1) fine-tuning the LM module on language-only reasoning traces, 2) PPO-based RL to refine the LM outputs, and 3) PPO-based tuning of the full model on a curated multimodal reasoning set to improve the cross-domain transfer of reasoning capabilities. This pipeline is used to tune an existing foundational model, Qwen2.5-VL, into a model for visual reasoning dubbed OVR-7b. Empirically, the authors show that this model achieves consistently better results on a range of visual reasoning benchmarks compared to other models of the same size. Analyzing the reasoning traces with GPT-4o-mini, the authors also show that steps (1)-(3) increase the occurrence of “cognitive behaviors” (e.g., re-inspection or tracing) in the reasoning traces of the model. Finally, the authors also present some ablations on different secondary questions, e.g., if the additional training is detrimental for the language/vision capabilities (unrelated from reasoning) or at what step of the procedure the transfer starts happening.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nThe paper is overall well-written, and the narration is easy to follow. The methodology proposed is sufficiently motivated and I am not aware of other works that proposed the same methods before. The authors present empirical evidence on the validity of their method. These includes benchmarking on standard language-only reasoning (mostly mathematical, AIME/MATH) and general (MMLU) benchmarks, as well a wide range of visual reasoning benchmarks (again, mostly mathematical-based reasoning). Furthermore, the paper also includes a quantification of the improvement of cognitive behaviors (through an automatic analysis of the reasoning traces), that appear to be consistently growing thanks to the 3-stage tuning procedure proposed in the paper.  \n\n\n**Weaknesses**\n\nThe main weakness that I can identify in this paper is the limited novelty factor of the proposed methodology. The three-stage training pipeline proposed to study the transfer of cognitive behaviors from language to multimodal inference is not characterized by significantly novel ideas compared to the techniques already present in the literature. Possibly, step (3) of the proposed pipeline (i.e., the multimodal RL for cross-modal adaptation) is the only original step in the method (or at least, I am not familiar with any other works doing it). On another note, I believe that the observation that improvements on mathematical and coding reasoning (resulting from RL on language-only data) can also directly benefit multimodal reasoning tasks is interesting but somehow expected.\n\nQUESTIONS:\n1. How do you justify the superior performances of OVR-7b on the language-only benchmarks in Table 1? For instance, why do you think it is the case the OVR-7b improves over the standard Qwen2.5-7b model on MMLU accuracy by 5.4?\n2. The ablation on the emergence of visual-specific cognitive behaviors is only performed on MathVision; would it be possible to extend it to the other visual reasoning datasets that are considered in Table 2 as well?\n3. Related to the ablation of steps (1)-(3), could you also provide an ablation not just on the presence of cognitive behaviors in the reasoning steps, but also on the actual performance on the different multimodal reasoning tasks? What I am missing right now is a table to quantify the progression from Qwen2.5-VL-7B to OVR-7b in terms of performances on the benchmarks for the different steps of your proposed pipeline.\n4. Could you reproduce/collect the results on all the datasets in Table 2 for Qwen2.5-VL-7B? This is the base model from which OVR-7b is obtained, and I believe it to be important to have a full picture of its performance.\n5. I think this missing reference could be quite relevant: VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning, Wang et al, 2025. Could you please comment on the differences between their and your work?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an interesting observation that the cognition behaviors trained from text-only cold start can transfer to vision reasoning tasks. Based on the finding, the authors design a three-stage training pipeline on cross-modality cognitive behavior training. The resulting model demonstrates strong reasoning ability across modalities. An in-depth analysis is also conducted to improve the understanding of multi-modal reasoning.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. This paper is clearly written and well-motivated\n2. The observation about cognitive behavior transfer is interesting\n3. The resulting model demonstrates strong performance on reasoning problems in both modality\n4. An in-depth analysis is provided about the cognition behavior transfer\n\nWeakness:\n1. The training framework is not well-explained. In Section 4.1, the authors propose a three-stage training, including  language-only cold start, language-only RL and multimodal RL. It's unclear why each stage is necessary, as we know that both \"language-only cold start\" and \"language-only RL\" incentivize the cognitive behaviors in reasoning. \n2. Lack of ablations/baselines. Build upon point 1, more ablation studies are needed to understand the training framework. For example, how much is the average gain on vision reasoning tasks, if we compare (state 1 + stage 2 + stage 3) with (stage 2 + stage 3) or stage 2/3 only training. Also, it'll be interested to see from empirical results whether stage 3 could enhance LLM reasoning or not.\n3. Lack of discussion and analysis. This paper contains a lot of discussion on cognition behaviors but lack discussion on the relationship between cognition behaviors and reasoning.\n\nQUESTIONS:\n1. What will be the performance if we train the model only with stage 2/3 or stage 2+3. Are all the 3 stages necessary?\n2. In table 2, many results on the section of \"RL-based methods\" are missing. What's the average gain of the model on vision-reasoning tasks and text-reasoning tasks, respectively?\n3. What's the relationship between cognition behaviors and reasoning? Does more cognition necessarily lead to better reasoning performance?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIt proposes a 3-stage training pipeline for multi-modal reasoning, where cold start training for linguistic reasoning happens first, followed by RL for linguistic reasoning and finally multi-modal reasoning.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Comprehensive evaluations\n- Strong performance compared to the baselines\n- Interesting research angle: it investigates an interesting question, even though the results are not sufficient to support the general claims made in this paper\n\nWeaknesses:\n- A big part of this submission seems to be a study contribution, but it lacks comparison to other training pipelines e.g. (1) Language cold start + multi-modal RL, (2) language cold start + multi-modal cold start + multi-modal RL; and additional settings that could be interesting to compare to e.g. (3) multi-modal cold-start + multi-modal RL; (4) language cold-start only with more training data; (5) multi-modal cold-start only with more training data\n  - The reviewer is curious what the emergence of cognitive behaviors look like for these settings above? \n- According to this paper, most transfer happens after the cold start training – is it because of the relative size of the data, the training method or something else? \n- The paper describes MMVet as a perception-centric benchmark, but it also includes many questions e.g. in the math split that are reasoning-heavy. As for perception-centric benchmark, What about performance on CV Bench and BLINK?\n- Lack of generalization: Are the findings generalizable to other models, other data sizes (e.g. larger data for stage2 or stage3 RL)? \n\nMinor: In the reward design, using exact matches for reward=1 seems a bit strict? Have the authors considered alternative designs?\n\nQUESTIONS:\n- According to this paper, most transfer happens after the cold start training – is it because of the relative size of the data, the training method or something else? \n- The paper describes MMVet as a perception-centric benchmark, but it also includes many questions e.g. in the math split that are reasoning-heavy. As for perception-centric benchmark, What about performance on CV Bench and BLINK?\n- Are the findings generalizable to other models, other data sizes (e.g. larger data for stage2 or stage3 RL)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the transference of cognitive behaviors from language-only training for vision language models. The authors propose that reasoning skills learned from language-only training can significantly improve a model's performance on visual reasoning tasks. They introduce a three-stage training pipeline to facilitate and study this transfer. The resulting model, named Open-Vision-Reasoner (OVR), is a 7B parameter model based on Qwen2.5-VL.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The central hypothesis—that core, abstract reasoning skills are modality-agnostic and can be transferred from language to vision—is compelling and aligns with theories of human cognition. The initial experiment showing that a language-only \"cold start\" boosts visual reasoning performance (Figure 1) is a strong and surprising finding that provides excellent motivation for the work.\n2. The proposed three-stage training pipeline is logical and well-structured. It allows for a systematic investigation of the central hypothesis and enables a clear ablative analysis (Table 5) to demonstrate the contribution of each stage. This methodical approach adds credibility to the findings.\n3. The paper demonstrates impressive results across a wide range of challenging language and visual reasoning benchmarks. OVR significantly outperforms other open-source 7B models on tasks like MathVision (+15.1%) and LogicVista (+6.5%), providing concrete evidence that the proposed training strategy is effective.\n4. The authors go beyond simply reporting benchmark scores. The analysis of both linguistic (Table 4) and visual-specific (Table 3) cognitive behaviors provides valuable insight into the model's internal changes. The attempt to quantify the \"Behavior Transfer Rate\" (BTR) is a novel contribution towards understanding the mechanisms of cross-modal generalization.\n\nWeaknesses:\n1. The paper provides no validation for the reliability or accuracy of GPT-4o-mini as a classifier for these nuanced behaviors. How often does it produce false positives or negatives? Without this, all quantitative claims about behavior frequency (e.g., in Tables 3 and 4) are built on an unverified foundation.\n2. The analysis cannot distinguish between a model that has genuinely learned a new cognitive process (e.g., verification) and a model that has simply learned to verbalize its outputs in a style that mimics verification, because such patterns were present in its training data (distilled from DeepSeek-R1). The model may be a more sophisticated \"stochastic parrot\" of reasoning steps rather than an actual reasoner. This is a critical distinction that the current analysis fails to address.\n3. The proposed three-stage training pipeline (SFT -> Language RL -> Multimodal RL) is more of a training recipe than a novel method. And such pipeline is well known in the community. The individual components are standard: PPO with GAE is a well-established algorithm, and a minimalist binary reward function for correctness is a common choice in reasoning tasks. While the application to study cognitive transfer is interesting, the work does not introduce a new algorithm, architecture, or fundamental learning principle, which limits its contribution for a top-tier venue.\n4. The paper's own results (Table 5) show a clear and significant trade-off between reasoning and perception. As the model's reasoning score on MME-R increases, its perception score on MME-P and MMVet consistently decreases from the baseline. The authors acknowledge this but frame it as a temporary issue to be addressed in future work. However, for a paper claiming to create a \"general-purpose multimodal reasoning\" system, sacrificing a fundamental capability like visual perception is a major flaw.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}