Title: QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Training
Venue: NeurIPS 2025
Accepted: yes
Keywords: machine learning

healthcare

reinforcement learning

multimodal learning

AI for healthcare

clinical AI

time series modeling

natural language processing
Abstract: Clinical decision‑making routinely demands reasoning over heterogeneous data, yet existing multimodal language models (MLLMs) remain largely vision‑centric and fail to generalize across clinical specialties. To bridge this gap, we introduce QoQ-Med-7B/32B, the first open generalist clinical foundation model that jointly reasons across medical images, time‑series signals, and text reports. QoQ-Med is trained with Domain‑aware Relative Policy Optimization (DRPO), a novel reinforcement‑learning objective that hierarchically scales normalized rewards according to domain rarity and modality difficulty, mitigating performance imbalance caused by skewed clinical data distributions. Trained on 2.61 million instruction tuning pairs spanning 9 clinical domains, we show that DRPO training boosts diagnostic performance by 43% in macro‑F1 on average across all visual domains as compared to other critic-free training methods like GRPO. Furthermore, with QoQ-Med trained on intensive segmentation data, it is able to highlight salient regions related to the diagnosis, with an IoU 10x higher than open models while reaching the performance of OpenAI o4-mini. To foster reproducibility and downstream research, we release (i) the full model weights, (ii) the modular training pipeline, and (iii) all intermediate reasoning traces.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents QoQ-Med-7B/32B, an open-source multimodal clinical foundation model that jointly processes medical images (2D/3D), time-series signals (ECG), and text reports. The key contribution is Domain-aware Relative Policy Optimization (DRPO), a hierarchical reinforcement learning method that scales rewards based on domain rarity and task difficulty to address data imbalance in heterogeneous clinical datasets. The model is trained on 2.61M instruction-tuning pairs across 9 clinical domains and demonstrates significant improvements over existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1. DRPO is a well-motivated extension of GRPO with clear theoretical foundation. The hierarchical scaling mechanism (domain-level and cluster-level) effectively addresses the critical problem of performance imbalance in multi-domain clinical learning. The mathematical formulation is rigorous and the algorithm design is sound.\n2. First open-source model to successfully integrate 1D time-series (ECG) with 2D/3D medical images, addressing a significant gap in existing medical MLLMs. The architecture elegantly handles missing modalities.\n3. The model generates reasoning traces and bounding boxes for localization, crucial for clinical trust. Qualitative examples with clinician annotations demonstrate practical utility, though more systematic evaluation is needed.\n\nWeaknesses: \n1. The paper's most significant limitation is the lack of statistical significance testing. All experiments are single-run due to computational constraints (acknowledged in checklist Q7), meaning no error bars or confidence intervals are provided. This fundamentally limits our confidence in the reported improvements, especially given that some gains are relatively modest. The paper also lacks systematic error analysis beyond a few qualitative examples. While Figure 4 shows some failure cases, there's no aggregate analysis of error types, failure modes, or when DRPO provides the most benefit versus baseline methods. The evaluation would be significantly strengthened by at least bootstrap-based confidence intervals on existing results and a more thorough characterization of model failures.\n2. Despite working with clinical data where fairness is critical, the paper provides no analysis of demographic or institutional bias. It's unclear whether DRPO's domain weighting helps reduce disparities or potentially amplifies existing biases by over-emphasizing certain underrepresented domains that may correlate with demographic factors. The paper also doesn't evaluate performance on truly rare conditions beyond the main imaging modalities, leaving questions about robustness in extreme low-resource settings. Without subgroup performance analysis or evaluation on out-of-distribution data, the clinical safety and equity implications remain unclear.\n3. The reasoning quality evaluation, while featuring clinician annotations, lacks methodological rigor. The paper doesn't specify how many clinicians participated, what their qualifications were, or what the inter-annotator agreement was. The evaluation appears to be purely qualitative on selected examples rather than systematic across the validation set. Furthermore, the use of BiomedParse model to generate pseudo-segmentation labels when ground truth is unavailable introduces another source of potential error that isn't validated. The heavy reliance on appendices for critical implementation details also makes it difficult to fully assess the methodology, as appendix content isn't peer-reviewed.\n\nQUESTIONS:\n1. Can you provide confidence intervals through bootstrap sampling of your existing results? What is the variance across different random seeds for at least a subset of experiments?\n2. What is the isolated contribution of cluster-level scaling? How sensitive are results to the number of clusters and reward weights?\n3. How does DRPO perform on the rarest conditions in your datasets? Does the hierarchical weighting risk overfitting to noise in extremely small domains?\n\nLIMITATIONS:\nThe authors briefly acknowledge the limited sample efficiency of unsupervised reasoning learning in their conclusion, but a more comprehensive discussion of limitations is warranted. The most critical limitation is the lack of statistical validation - all experiments are single-run due to computational constraints, preventing any assessment of variance or statistical significance of the reported improvements. This is particularly concerning given that some performance gains are modest and could potentially fall within noise margins. The paper also lacks analysis of potential biases that DRPO's hierarchical weighting might introduce or amplify. By up-weighting underrepresented domains, the method could inadvertently overfit to spurious correlations in small datasets or amplify existing demographic biases present in those domains. The computational requirements (8xA100 GPUs for multiple days of training) severely limit accessibility and reproducibility, yet the environmental impact is not discussed. Additionally, while the model generates reasoning traces, there's no systematic evaluation of their clinical accuracy beyond selected examples, and no prospective clinical validation is provided. The reliance on pseudo-labels from BiomedParse for segmentation evaluation introduces another unvalidated source of error. Finally, the heavy dependence on appendices for critical implementation details means that key aspects of the method cannot be fully evaluated within the peer-review process, potentially hiding important limitations or implementation choices that could affect reproducibility and generalization.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces QoQ-Med, a novel multimodal foundation model for clinical applications, uniquely capable of reasoning over diverse data types including medical images (2D/3D), time-series signals (ECG), and text. The core technical contribution is a new reinforcement learning algorithm, Domain-aware Group Relative Policy Optimization (DRPO). DRPO is designed to address the critical challenge of data heterogeneity and imbalance in clinical datasets by introducing a hierarchical reward scaling mechanism. The authors demonstrate that DRPO significantly improves diagnostic performance over standard RL methods, particularly in underrepresented domains. The resulting model, which generates both a final diagnosis and interpretable outputs like reasoning traces and salient region highlighting, is shown to be competitive with or superior to existing open-source and even some closed-source models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n---\n\n- The paper tackles a significant and challenging problem, which is creating a generalist clinical foundation model that handles truly heterogeneous data. The integration of 1D time-series data (ECG) with 2D/3D imaging and text is a notable step forward from vision-centric medical MLLMs and addresses a real clinical need for holistic patient data analysis.\n\n- The proposed DRPO algorithm is a novel and well-motivated contribution. It provides a principled way to handle domain imbalance within a critic-free RL framework, which is computationally efficient. The hierarchical scaling based on both domain rarity and intra-domain difficulty is an intelligent design that directly addresses a known problem in training on real-world. The empirical results, showing up to a 43% macro-F1 score improvement, strongly support its effectiveness.\n\n- The experimental evaluation is comprehensive and well-designed. The authors compare their method against a wide range of strong baselines (SFT, PPO ...). They evaluate on a large, multi-domain dataset and include specific experiments to validate multimodal fusion and interpretability. The ablation studies clearly demonstrate the contribution of each component of DRPO.\n\n- Strong comparison against other publicly available systems in the Table 4.\n\n- The authors plan to release the model weights (7B and 32B), the modular training pipeline and the extensive reasoning traces is a major strength. This significantly enhances reproducibility and will provide a valuable resource for the research community, lowering the barrier to entry for research.\n\nWeaknesses:\n---\n\nN/A\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nA minor limitation is that while the model handles multiple modalities, the fusion mechanism is a relatively simple concatenation of projected tokens, a more sophisticated fusion mechanism might yield further improvements, though this is more of a future work direction than a flaw.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents QoQ-Med, a multimodal clinical foundation model that processes medical images (2D/3D), time-series signals (ECG), and text. The model is trained with Domain-aware Relative Policy Optimization (DRPO), a new reinforcement learning objective that extends GRPO. DRPO boosts diagnostic performance and hierarchically scales rewards based on domain rarity and example difficulty to balance training.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Multimodality: The proposed model, QoQ-Med, is significant as it is one of the first open-source foundation models to jointly process 1D time-series data (ECG) alongside 2D/3D medical images and text.\n2. Comprehensive evaluation: The experimental evaluation is thorough and convincing. The results show that DRPO achieves substantial performance improvements (e.g., a 43% increase in macro-F1) over established methods. The ablation studies effectively isolate and validate the contributions of DRPO's key components.\n3. Writing: The paper is well-written and clearly structured. The methodology is explained logically, and Figure 1 provides an intuitive overview of both the model architecture, greatly enhancing the reader's understanding.\n\nWeaknesses\n1. Significance analysis: The primary weakness is a lack of statistical significance analysis for the experimental results. Given the high variance often seen in RL training, reporting error bars or confidence intervals from multiple runs would be crucial to substantiate the robustness of the claimed performance gains.\n2. Hyperparameters: The design of the combined reward function involves several weighting hyperparameters (e.g., $\\lambda_{acc}, \\lambda_{IoU}, \\lambda_{aux}$). The paper does not provide a sensitivity analysis or justification for the specific values chosen, leaving it unclear how performance might be affected by different choices.\n\nQUESTIONS:\n1. Availability of the Reasoning Traces Dataset: The commitment to release the 2.61 million reasoning traces is a significant and commendable contribution that would greatly benefit the research community. However, upon reviewing the provided anonymous link, I was unable to locate this specific dataset. Could the authors please clarify the status of this resource or provide a direct pointer to it?\n2. Analysis of Reasoning and Failure Modes: Figure 4 provides compelling qualitative examples of QoQ-Med's successful reasoning process, which is a key strength. To offer a more comprehensive and balanced assessment of the model's true capabilities and limitations, it would be highly beneficial to include an analysis of its common failure modes. For instance:\n  - Are there recurring patterns when the generated reasoning is clinically incorrect or irrelevant?\n  - Could the authors provide examples where the reasoning seems plausible but is factually disconnected from the input image?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a QoQ-Med model, a large Multimodal Clinical Foundation Model with Domain-Aware GRPO Training. The model takes medical images, time-series signals, and text reports as inputs. It also introduces a novel reinforcement learning method, Domain-aware Relative Policy Optimization (DRPO), which adjusts rewards based on domain rarity and modality difficulty to address data imbalance. The model is trained on 2.61 million instruction-tuning samples from 9 clinical domains. Experimental results show strong performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The multimodal inputs setup closely mirrors real clinical diagnostic reasoning, going beyond standard VQA\n2. The model is trained on high-quality data, including 2.61 million question-answer pairs with intermediate reasoning\n3. The method employs the SOTA GRPO in the model training\n4. Experimental results look very promising.\n\nWeaknesses:\n1. The logic in clinical diagnosis varies significantly in different fields. Please discuss more on the applicability and adaptability of QoQ-style training across domains with differing reasoning structures.\n2. The study lacks expert human evaluation or annotation, such as rating answer helpfulness or diagnostic correctness.\n3. Although the model supports images, text, and time-series, there is little analysis on how easily the framework scales to additional modalities (e.g., pathology slides, genomic data, 3D scans).\n\nQUESTIONS:\n1. The logic in clinical diagnosis varies significantly in different fields. Please discuss more on the applicability and adaptability of QoQ-style training across domains with differing reasoning structures.\n2. Although the model supports images, text, and time-series, there is little analysis on how easily the framework scales to additional modalities (e.g., pathology slides, genomic data, 3D scans). Please discuss how to extend the model on further modality data/inputs.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-19"}