project
XBai o4 - An open-source parallel inference model with high-quality inference trajectories.
XBai o4 is an open-source large language model trained on "reflection-generated forms" and combining long CoT reinforcement learning and procedural reward learning. It performs well in complex reasoning and has surpassed OpenAI-o3-mini in medium mode.
What is XBai o4?
XBai o4 is an open-source large language model trained using "reflection-generative forms," combining long CoT reinforcement learning and procedural reward learning. It excels in complex reasoning capabilities, surpassing OpenAI-o3-mini in medium-mode performance. XBai o4's backbone network, based on shared PRMs and a policy model, significantly reduces inference costs. The model performs exceptionally well on multiple benchmarks, such as AIME24 and LiveCodeBench v5. It supports single-node and multi-node training, provides detailed installation and evaluation procedures, and offers developers powerful tools and flexible usage methods.
XBai o4's main functions
- Complex reasoning abilityIt can handle complex logical reasoning and mathematical problems involving multiple steps and generate high-quality reasoning trajectories.
- Efficient ReasoningBased on a backbone network of shared PRMs and policy models, inference costs are significantly reduced and inference efficiency is improved.
- Multilingual supportIt supports multiple languages, can process and generate high-quality text content, and is suitable for a variety of natural language processing tasks.
- Flexible training and deploymentIt provides detailed training and deployment guidelines, supports single-node and multi-node training, and makes it convenient for developers to train models according to hardware conditions.
- Multi-task learningTraining is conducted by combining multiple tasks, including language modeling, mathematical reasoning, and logical reasoning, to improve the model's generalization ability and adaptability.
XBai o4's technical principles
- Reflective Generation FormXBai o4 is trained using "reflection-based generation" and combined with "long CoT (Chain of Thought) reinforcement learning" and "process reward learning," enabling the model to simultaneously achieve deep reasoning and the selection of high-quality reasoning trajectories.
- Process Reward LearningProcess reward learning is a reinforcement learning method that leverages the performance of reward models during inference to help models better learn intermediate steps in the inference process and improve overall inference ability. XBai-o4 further optimizes the inference process and reduces computational costs based on a backbone network of shared PRMs and policy models.
- Multi-task learningThe model incorporates multiple tasks during training, including language modeling, mathematical reasoning, and logical reasoning. This multi-task learning approach enables the model to better adapt to different application scenarios and improves its generalization ability. Evaluations on multiple benchmarks demonstrate its superior performance across various tasks.
- High-efficiency inference architectureThe model employs an efficient inference architecture, improving inference speed by optimizing the model's structure and computational process. For example, the model supports multiple inference modes, allowing users to select the appropriate mode based on specific needs, balancing inference speed and accuracy. The model provides detailed inference processes and evaluation methods, facilitating optimization and adjustments by users in practical applications.
XBai o4's project address
- GitHub repository: https://github.com/MetaStone-AI/XBai-o4/
- HuggingFace model libraryhttps://hf-mirror.com/MetaStoneTec/XBai-o4
Application scenarios of XBai o4
- EducationIt assists in teaching, providing students with solutions to complex mathematical and logical problems, and helping users better understand the problem-solving process.
- Research supportIn scientific research, it is used for literature reviews, generating experimental design ideas, and reasoning and analyzing complex scientific problems.
- Programming aidsIt can provide developers with suggestions on code generation, logical reasoning, and troubleshooting, improving programming efficiency and code quality.
- Content creationIt can quickly generate high-quality text content in copywriting and creative writing, inspiring creators.
- Intelligent Customer ServiceTo provide users with accurate answers and solutions to their questions, thereby improving customer service efficiency and user experience.