MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems

Reinforcement Learning (RL)-Based Recommender Systems (RSs) are increasingly recognized for their ability to improve long-term user engagement. Yet, the field grapples with challenges such as the absence of accessible frameworks, inconsistent evaluation standards, and the complexity of replicating prior work. Addressing these obstacles, we present EasyRL4Rec, a user-friendly and efficient library tailored for RL-based RSs. EasyRL4Rec features lightweight, diverse RL environments built on five widely-used public datasets, and is equipped with comprehensive core modules that offer rich options to ease the development of models. It establishes consistent evaluation criteria with a focus on long-term impacts and introduces customized solutions for state modeling and action representation tailored to recommender systems. Additionally, we share valuable insights gained from extensive experiments with current methods. EasyRL4Rec aims to facilitate the model development and experimental process in the domain of RL-based RSs. The library is openly accessible at https://github.com/chongminggao/EasyRL4Rec.