Title: Praxis-VLM: Vision-Grounded Decision Making via Text-Driven Reinforcement Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multimodal Decision Making

Situational Reasoning

Vision-Language Model Reasoning
Abstract: Vision Language Models exhibit impressive performance for various tasks, yet they often lack the sophisticated situational reasoning required for complex decision-making. This paper shows that VLMs can achieve surprisingly strong decision-making performance when visual scenes are replaced by textual descriptions, suggesting foundational reasoning can be effectively learned from language. Motivated by this insight, we propose Praxis-VLM, a reasoning VLM for vision-grounded decision-making. Praxis-VLM employs the GRPO algorithm on textual scenarios to instill robust reasoning capabilities, where models learn to evaluate actions and their consequences. These reasoning skills, acquired purely from text, successfully transfer to multimodal inference with visual inputs, significantly reducing reliance on scarce paired image-text training data. Experiments across diverse decision-making benchmarks demonstrate that Praxis-VLM substantially outperforms standard supervised fine-tuning, exhibiting superior performance and generalizability. Further analysis confirms that our models engage in explicit and effective reasoning, underpinning their enhanced performance and adaptability.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates vision-grounded decision-making. The central insight is that the visual scenes can be better understood by first generating a detailed description of the scene, and then reason about the decision based on the description. The proposed Praxis-VLM is trained using a GRPO algorithm with an adaptive reward function\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: see below; Weaknesses: see Questions and Limitation\n\n1.\tThe empirical discovery is interesting (though less surprising), and it also shows the current, even SOTA VLMs, fall short in multimodal understanding. It may provide an alternative approach for a more data-efficient method of reasoning acquisition, bypassing the needs for image-text-reasoning pairs.\n2.\tThe performances seem to be good.\n3.\tThe paper provides thorough analysis, including the role of reasoning length, error cases, clustering of reasoning type, etc.\n\nQUESTIONS:\n1.\tWhile the approach transfers reasoning from vision to texts, it doesn't directly optimize visual grounding during training: Line 126, “during the training phase, visual inputs are replaced by their textual descriptions, and only the language model components of the VLM are updated. This leverages the scalability of text data for knowledge acquisition. Yet, during the inference phase, the entire trained VLM architecture, including the vision encoder, is used to process the image-text input pair”. \n2.\tThe performance of GRPO may rely heavily on the backbone VLM (Qwen2.5-VL as a strong open-source model).\n\nLIMITATIONS:\n1.\tThe data generation relies heavily on the backbone VLM (GPT-4o). the quality and diversity of synthetic scenarios are not deeply discussed. There's room to explore how scenario variety or GPT hallucination might affect training.\n2.\tGRPO, multi-stage training, and adaptive reward design are used to improve reasoning. Although effective, it is an established approach and may focus more on the engineering perspective.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper proposes the VLM-based reasoning system, Praxis-VLM, that relies on text descriptions of the visual context (an image) in the vision-grounded decision-making process. Praxis-VLM uses reinforcement learning (GRPO method) to learn how to evaluate possible actions and their outcomes. The paper demonstrates that strong visual reasoning capabilities can be accomplished when the visual context (an image) is represented only through a pure textual description.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The idea of using merely text descriptions of visual scenes to impose effective visual reasoning and consequent decion-making is sound and interesting.
2. The proposed Praxis-VLM shows its upper-hand over the baselines (base VLMs) in the experimental evaluation.
3. The proposed approach can be of value in the taks for which the available (image, text) training pairs are scarce.

Weaknesses:
1. The experimental evaluation is limited to one VLM model in two size versions (3B and 7B parameters). 
2. It is not clear from the paper how a detailed (complete in a semantic sense) descriptions are imposed when using GPT-4o.

QUESTIONS:
1. It will be interesting to see the results of Praxis-VLM built based on other popular VLMs. It is probable that the results (degree of improvement, if any) might differ from those achieved using Qwen2.5-VL.
2. How one can be sure, without concept leaking in the prompt formulation, that a textual description of an image generated by GPT- 4o is sufficient and accurate in a given decision-making context?
3. Do you have any suggestion why there is no difference between „Major” and „Pass@1” scores of Qwen2.5-VL-7B on VIVA dataset?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Praxis-VLM, a framework that enhances the decision-making and reasoning abilities of VLMs by training them on text-only decision-making scenarios. The core idea is to first build reasoning capabilities in a purely textual environment and then transfer these skills to multimodal inference. The method leverages GRPO and introduces an adaptive reward system (focusing on format, accuracy, and reasoning length at different training stages). Notably, Praxis-VLM achieves better performance than standard VLMs and SFT baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The idea of using text-only reasoning to bootstrap visual decision-making is nice and data-efficient, especially given the scarcity of image-text reasoning datasets.\n\n2.  The multi-stage GRPO training with adaptive rewards is well-motivated and clearly described, focusing on progressively harder reasoning tasks.\n\n3. Leveraging GPT-4o to synthetically generate a high-quality, diverse text-only dataset reduces the reliance on expensive manual annotations.\n\n4. Praxis-VLM consistently outperforms vanilla VLMs and SFT baselines, even if the margins are modest (~3-4%).\n\nWeaknesses:\n\n1.  Dataset Generation Not Fully Discussed: The process of creating the synthetic dataset relies heavily on GPT-4o with a small set of seed prompts. It’s unclear how representative, diverse, or potentially biased this generated data is. Also, manual effort in seed question crafting might limit scalability.\n\n2. Lack of Ablations on Visual Inputs: The paper does not explore what would happen if image-to-text data (captions or descriptions derived from images) were used instead of purely synthetic text-to-text scenarios.\n\n3. Reward Design: The adaptive reward seems similar to prior GRPO training setups. The novelty here feels incremental, especially since no in-depth reward ablation is provided to justify the significance of each reward component (especially the Rlen reward, whose importance is assumed but not convincingly demonstrated).\n\n4. Limited Performance Gains: The performance improvement over the baselines is modest (3-4%), raising questions about the practical significance versus the added complexity.\n\n5. Results: The cold-start initialization and math-focused training did not show significant advantages in some cases, but the paper does not sufficiently explain why.\n\n6. SFT Underperformance: It’s surprising that SFT and reason with SFT sometimes performs worse than the base model, which raises questions about dataset or training stability but is not addressed.\n\nQUESTIONS:\n1. Dataset Requirements: How scalable is the dataset generation process? How much manual intervention is required in crafting seeds, and could this limit transfer to other domains?\n\n2. Visual Context Integration: Why not explore using external tools (e.g., image captioners) to generate text from visual scenes instead of relying solely on synthetic text? Could that have improved the visual-text alignment?\n\n3. Ablation on Visual vs. Text Reasoning: Can you provide results where image-derived text is used during training instead of purely text-generated scenarios? Would this still achieve the same level of reasoning transfer?\n\n4. Role of Rlen: You report that increasing reasoning length sometimes decreases accuracy. Could you provide a detailed ablation isolating the impact of Rlen, and show which reward components ultimately matter most in driving performance gains?\n\n5. Relative Contributions: How much of the final performance improvement comes from text-to-text reasoning versus the GRPO training? Given the small gains, is it possible that most of the improvement is from the RL fine-tuning alone?\n\n6. Adaptive Reward Novelty: How does your adaptive reward meaningfully differ from prior GRPO reward structures? It feels largely similar to other multi-stage RL schemes.\n\n7. SFT Anomalies: Why does SFT sometimes perform worse than the base model? Is this due to overfitting on synthetic text or some limitation in the data quality?\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces Praxis-VLM, a vision-language model trained to perform vision-grounded decision-making through text-only reinforcement learning. Instead of using paired image-text data, the model learns reasoning from synthetic textual scenarios using the GRPO algorithm and an adaptive reward scheme across two training stages. At inference time, it applies these reasoning skills to real visual inputs. Experiments on three benchmarks show Praxis-VLM outperforms supervised fine-tuning baselines and generalizes well across tasks. The authors argue that foundational decision-making skills can be effectively learned from language alone and transferred to visual contexts.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- **Innovative Use of Text-Only Training**: The paper presents a cost-efficient alternative to multimodal training by using synthetic text-only data to develop reasoning skills for vision-language models.\n\n- **Strong Empirical Results and Generalization**: Praxis-VLM consistently outperforms strong baselines across diverse tasks, including an out-of-domain benchmark, demonstrating both effectiveness and robustness.\n\n- **Clear Evidence of Reasoning Quality In Experiment**: The paper includes thoughtful qualitative and quantitative analysis, such as reasoning type clustering and diverse decoding evaluations, which show the model produces multi-dimensional, structured reasoning that improves decisions.\n\nWeaknesses:\n- **Evaluation Focused on Multiple-Choice QA Format**: All benchmarks used (VIVA, PCA-Bench, EgoNormIA) are framed as multiple-choice QA tasks. This narrow task format may limit the generality of conclusions. The model’s ability to reason in open-ended or interactive settings (e.g., planning, dialogue-based decision making, robotic control) is not tested.\n\n- **Lack of Analysis on Visual Feature Usage**: Although the model uses vision encoders at inference time, it is unclear how much the model actually relies on visual input versus prior learned textual knowledge. There is no attention or attribution analysis showing whether the model grounds its reasoning in visual evidence or simply applies text-trained patterns.\n\nQUESTIONS:\n- **Robustness to Visual Understanding Errors**: How does the model perform when image descriptions are imperfect or noisy? Is its performance sensitive to vision encoder quality?\n\n- **Synthetic Data Quality and Coverage**: How diverse and representative are the GPT-4-generated scenarios? Could performance drop if the text data lacks variety or introduces bias?\n\n- **Role of the Math Pretraining Stage**: What specific benefit does Stage 1 contribute beyond output formatting? Are there measurable improvements in reasoning depth or structure?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-05"}