MedFact-R1: Towards Factual Medical Reasoning via Pseudo-Label Augmentation

• URL: http://arxiv.org/abs/2509.15154v1
• Date: Thu, 18 Sep 2025 16:59:59 GMT
• Title: MedFact-R1: Towards Factual Medical Reasoning via Pseudo-Label Augmentation
• Authors: Gengliang Li, Rongyu Chen, Bin Li, Linlin Yang, Guodong Ding,
• Abstract summary: MEDFACT-R1 is a two-stage framework that integrates external knowledge grounding with reinforcement learning.<n>It delivers up to 22.5% absolute improvement in factual accuracy over previous state-of-the-art methods.
• Score: 25.186622292311228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring factual consistency and reliable reasoning remains a critical challenge for medical vision-language models. We introduce MEDFACT-R1, a two-stage framework that integrates external knowledge grounding with reinforcement learning to improve the factual medical reasoning. The first stage uses pseudo-label supervised fine-tuning (SFT) to incorporate external factual expertise; while the second stage applies Group Relative Policy Optimization (GRPO) with four tailored factual reward signals to encourage self-consistent reasoning. Across three public medical QA benchmarks, MEDFACT-R1 delivers up to 22.5% absolute improvement in factual accuracy over previous state-of-the-art methods. Ablation studies highlight the necessity of pseudo-label SFT cold start and validate the contribution of each GRPO reward, underscoring the synergy between knowledge grounding and RL-driven reasoning for trustworthy medical AI. Codes are released at https://github.com/Garfieldgengliang/MEDFACT-R1.

Related papers

• MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimization [46.65200216642429]
  We introduce MedAD-38K, the first large-scale, multi-modal, and multi-center benchmark for MedAD featuring diagnostic Chain-of-Thought (CoT) annotations alongside structured Visual Question-Answering (VQA) pairs.<n>Our proposed model, MedAD-R1, achieves state-of-the-art (SOTA) performance on the MedAD-38K benchmark, outperforming strong baselines by more than 10%.
  arXiv  Detail & Related papers  (2026-02-01T07:56:10Z)
• Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation [97.36081721024728]
  We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations.<n>Our benchmark unifies three types of medical data for open-ended diagnostic generation.<n>We present MedConf, an evidence-grounded linguistic self-assessment framework.
  arXiv  Detail & Related papers  (2026-01-22T04:51:39Z)
• MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning [52.064286116035134]
  We develop MedAlign, a framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA)<n>We first propose a multimodal Direct Preference Optimization (mDPO) objective to align preference learning with visual context.<n>We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM.
  arXiv  Detail & Related papers  (2025-10-24T02:11:05Z)
• Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning [6.778254993886297]
  We introduce Fleming-R1, a model designed for verifiable medical reasoning through three complementary innovations.<n>First, our Reasoning-Oriented Data Strategy (RODS) combines curated medical QA datasets with knowledge-graph-guided synthesis.<n>Second, we employ Chain-of-Thought (CoT) cold start to distill high-quality reasoning trajectories from teacher models.<n>Third, we implement a two-stage Reinforcement Learning from Verifiable Rewards framework.
  arXiv  Detail & Related papers  (2025-09-18T13:35:14Z)
• MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation [55.37355146924576]
  MedSeqFT is a sequential fine-tuning framework for medical image analysis.<n>It adapts pre-trained models to new tasks while refining their representational capacity.<n>It consistently outperforms state-of-the-art fine-tuning strategies.
  arXiv  Detail & Related papers  (2025-09-07T15:22:53Z)
• Breaking Reward Collapse: Adaptive Reinforcement for Open-ended Medical Reasoning with Enhanced Semantic Discrimination [5.685365519519041]
  Reinforcement learning with rule-based rewards has demonstrated strong potential in enhancing the reasoning and generalization capabilities of vision-language models (VLMs) and large language models (LLMs)<n>Existing reinforcement fine-tuning (RFT) approaches in this domain primarily target closed-ended visual question answering (VQA)<n>We propose ARMed, a novel RL framework for open-ended medical VQA.
  arXiv  Detail & Related papers  (2025-08-18T14:31:26Z)
• MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph [57.54231831309079]
  We introduce MedKGent, a framework for constructing temporally evolving medical Knowledge Graphs.<n>We simulate the emergence of biomedical knowledge via a fine-grained daily time series.<n>The resulting KG contains 156,275 entities and 2,971,384 relational triples.
  arXiv  Detail & Related papers  (2025-08-17T15:14:03Z)
• MIRA: A Novel Framework for Fusing Modalities in Medical RAG [6.044279952668295]
  We introduce the Multimodal Intelligent Retrieval and Augmentation (MIRA) framework, designed to optimize factual accuracy in MLLM.<n>MIRA consists of two key components: (1) a calibrated Rethinking and Rearrangement module that dynamically adjusts the number of retrieved contexts to manage factual risk, and (2) A medical RAG framework integrating image embeddings and a medical knowledge base with a query-rewrite module for efficient multimodal reasoning.
  arXiv  Detail & Related papers  (2025-07-10T16:33:50Z)
• Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains [52.86636270242863]
  This work moves beyond the final-answer accuracy and investigates step-by-step reasoning in the medical and mathematical domains.<n>We introduce a fine-grained evaluation framework that judges the correctness of knowledge used and the quality of reasoning.<n>Using this framework, we study R1-distilled and base Qwen models trained with supervised fine-tuning (SFT) and/or reinforcement learning (RL) in the medical and math domains.
  arXiv  Detail & Related papers  (2025-06-02T18:01:00Z)
• Improving Medical Reasoning with Curriculum-Aware Reinforcement Learning [2.262453679768892]
  We introduce textbfMedCCO, the first multimodal reinforcement learning framework tailored for medical VQA.<n>MedCCO is fine-tuned on a diverse set of close-ended medical VQA tasks to establish domain-grounded reasoning capabilities.<n>We validate MedCCO across eight challenging medical VQA benchmarks, spanning both close-ended and open-ended settings.
  arXiv  Detail & Related papers  (2025-05-25T16:20:55Z)
• Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA [17.823588070044217]
  We propose Discuss-RAG, a plug-and-play module designed to enhance the medical question answering system.<n>Our method introduces a summarizer agent that orchestrates a team of medical experts to emulate multi-turn brainstorming, thereby improving the relevance of retrieved content.<n> Experimental results on four benchmark medical QA datasets show that Discuss-RAG consistently outperforms MedRAG.
  arXiv  Detail & Related papers  (2025-04-30T01:37:44Z)
• Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models [6.176432104264649]
  Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored.<n>We propose Med-R1, a reinforcement learning (RL)-enhanced vision-language model designed to improve generalization and reliability in medical reasoning.<n>We evaluate Med-R1 across eight distinct medical imaging modalities.
  arXiv  Detail & Related papers  (2025-03-18T06:12:38Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• MedCoT: Medical Chain of Thought via Hierarchical Expert [48.91966620985221]
  This paper presents MedCoT, a novel hierarchical expert verification reasoning chain method.<n>It is designed to enhance interpretability and accuracy in biomedical imaging inquiries.<n> Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-12-18T11:14:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.