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  • Med-RewardBench: Benchmarking Reward Models and Judges for Medical . . .
    To address this, we introduce Med-RewardBench, the first benchmark specifically designed to evaluate MRMs and judges in medical scenarios Med-RewardBench features a multimodal dataset spanning 13 organ systems and 8 clinical departments, with 1,026 expert-annotated cases
  • Med-RewardBench：首个医学多模态大模型奖励模型与评估 . . .
    为此，来自深圳大学、香港城市大学、中国人民大学等机构的研究者们提出了 Med-RewardBench，这是 首个 专门用于在医学场景中评估 奖励模型 （Reward Models）和评估者（Judges）的基准。
  • Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified . . .
    We introduce Med-PRM, a process reward modeling framework that leverages retrieval-augmented generation to verify each reasoning step against established medical knowledge bases
  • MedicalGPT: Training Medical GPT Model - GitHub
    MedicalGPT training medical GPT model with ChatGPT training pipeline, implemantation of Pretraining, Supervised Finetuning, RLHF (Reward Modeling and Reinforcement Learning) and DPO (Direct Preference Optimization)
  • Med-RewardBench: Benchmarking Reward Models and . . .
    We evaluate 32 state-of-the-art MLLMs, including open-source, proprietary, and medical-specific models, revealing substantial challenges in aligning outputs with expert judgment Additionally, we develop baseline models that demonstrate substantial performance improvements through fine-tuning
  • Med-RewardBench: Benchmarking Reward Models and Judges for Medical . . .
    We evaluate 32 state-of-the-art MLLMs, including open-source, proprietary, and medical-specific models, revealing substantial challenges in aligning outputs with expert judgment Additionally, we develop baseline models that demonstrate substantial performance improvements through fine-tuning
  • MedicalGPT：基于LLaMA-13B的中英医疗问答模型（LoRA . . .
    The Ziya-LLaMA-13B-v1 is a large-scale pre-trained model based on LLaMA with 13 billion parameters It has the ability to perform tasks such as translation, programming, text classification, information extraction, summarization, copywriting, common sense Q A, and mathematical calculation
  • Med-PRM
    MED-PRM introduces a new reward model for evaluating intermediate reasoning steps in clinical question-answering Unlike traditional outcome-based supervision, MED-PRM uses retrieved documents to verify each reasoning step, enabling fine-grained, evidence-aligned training signals
  • RewardModeling在医疗领域中的应用_reward modeling 使用 . . .
    本文介绍了强化学习和RewardModeling在医疗领域的应用，通过奖励函数建模帮助智能体学习最优诊断和治疗策略。 核心概念包括状态、行动、奖励，通过具体代码实例展示了如何使用Q-learning实现。 文章探讨了实际应用场景、工具推荐及未来发展趋势。 1 背景介绍 随着人工智能技术的不断发展，其在医疗领域的应用也越来越广泛。 从诊断疾病、辅助治疗、药物研发到患者管理等方面，人工智能都在发挥着重要作用。 其中，强化学习作为人工智能的一个重要分支，也在医疗领域取得了显著的成果。 强化学习是一种通过与环境交互来学习最优行为策略的方法。 在强化学习中，智能体 (agent)通过采取行动 (action)来影响环境 (state)，并从环境中获得奖励 (reward)。
  • ZachariahPang medical_reward_model · Hugging Face
    This is the official reward model developed in the paper "Expert of Experts Verification and Alignment (EVAL) Framework for Large Language Models Safety in Gastroenterology"





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