Large language models (LLMs) are increasingly deployed as intelligent agents capable of executing complex real-world tasks through external tool interactions, but effective tool selection remains challenging due to the inherent limitations of real-world training data. These datasets suffer from severe tool imbalance following long-tail distributions, data scarcity for specialized tools, logic conflicts between user queries and available tools, rapidly evolving toolsets, and the presence of subpar samples including partially correct and dirty examples. Existing supervised fine-tuning (SFT) approaches struggle with these multifaceted challenges as they require abundant high-quality data, treat all labeled examples as ground truth regardless of quality, and lack the flexibility to generalize beyond specific query-tool pairings seen during training. While reinforcement learning (RL) offers a promising alternative through outcome-based learning, vanilla approaches like Group Relative Policy Optimization (GRPO) suffer from training instability due to conflicting reward signals and inefficient learning from weak signals. To address these issues, we propose TOOL-CURE, a novel method with two key improvements to GRPO: Proficiency-Scaled Curriculum Learning (PSCL), which organizes training into a two-stage curriculum that builds foundational skills on easier samples before progressing to harder ones, and Online Policy Guarding via Sample Screening (OPGSS), which continuously assesses rollout quality and masks dirty samples to prevent noisy gradients from destabilizing policy updates. Our approach enables stable and efficient learning from heterogeneous real-world data, resulting in a robust tool-selection agent that demonstrates significant improvements in accuracy and generalization capability. Our code is available at: https://github.com/einnullnull/TOOL-CURE.git.

Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form question-answering (LFQA). Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. Specifically, we first propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers and construct two datasets accordingly. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework, which contains automatic evaluation and reward modeling in different levels of granularity. Our generic framework comprises conventional fine-grained RLHF methods as special cases. Extensive experiments verify the superiority of our proposed Factuality-optimized RAG (FoRAG) method on both English and Chinese benchmarks. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics (i.e., coherence, helpfulness, and factuality), while the number of parameters is much smaller (only 1/24 of that of WebGPT-175B). Our datasets and models are made publicly available for better reproducibility.https://huggingface.co/forag łabelfootnote_dataset_url

Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis. Although impressive results have been achieved, we find that existing benchmarks do not reflect the complexity of real medical reports and specialized in-depth reasoning capabilities. In this work, we establish a comprehensive benchmark in the field of medical specialization and introduced RJUA-MedDQA, which contains 2000 real-world Chinese medical report images poses several challenges: comprehensively interpreting imgage content across a wide variety of challenging layouts, possessing the numerical reasoning ability to identify abnormal indicators and demonstrating robust clinical reasoning ability to provide the statement of disease diagnosis, status and advice based on a collection of medical contexts. We carefully design the data generation pipeline and proposed the Efficient Structural Restoration Annotation (ESRA) Method, aimed at restoring textual and tabular content in medical report images. This method substantially enhances annotation efficiency, doubling the productivity of each annotator, and yields a 26.8% improvement in accuracy. We conduct extensive evaluations, including few-shot assessments of 5 LMMs which are capable of solving Chinese medical QA tasks. To further investigate the limitations and potential of current LMMs, we conduct comparative experiments on a set of strong LLMs by using image-text generated by ESRA method. We report the performance of baselines and offer several observations: (1) The overall performance of existing LMMs is still limited; however LMMs more robust to low-quality and diverse-structured images compared to LLMs. (3) Reasoning across context and image content present significant challenges. We hope this benchmark helps the community make progress on these challenging tasks in multi-modal medical document understanding and facilitate its application in healthcare. Our dataset will be publicly available for noncommercial use at https://github.com/Alipay-Med/medDQA_benchmark.git