研究人员首先设计了推理的交错格式(interleaving format),为来自GSM8k和MATH数据集的数学问题策划相应的交互式工具使用轨迹(interactive tool-use trajectories),然后在高质量注释(high-quality annotations)上应用模仿学习(imitation learning),从而取得比任何现有的开源模型更好的性能。 而且,由于选取的数据远远没有涵盖一个...
= "add" description = "Adds two numbers together" args_schema: Optional[Type[BaseModel]] = AddInput return_direct: bool = True def _run( self, a: int, b: int, ) -> int: return a + b add_tool = AddTool() print(add_tool.name) print(add_tool.description) print(add_tool.args...
AlphaMath: Use MCTS to synthesize tool-integrated reasoning paths and step-level reward labels, then train the model with a multi-task language model and reward model loss to get a policy-and-value model. Compared with DeepSeekMath-7B-RL (58.8% pass@1) on MATH, AlphaMath catches up by...
For tool-calling inference and evaluation, please see the agent section. ModelMATHMATH-PythonGSM8K MiniCPM-2B 10.2 - 53.8 InternLM2-Math-Plus-1.8B 37.0 41.5 58.8 InternLM2-Math-7B 34.6 50.9 78.1 Deepseek-Math-7B-RL 51.7 58.8 88.2 InternLM2-Math-Plus-7B 53.0 59.7 85.8 InternLM2-Math-...
(PoT) rationales, and also ensures extensive coverage of diverse fields in math. The hybrid of CoT and PoT not only unleashes the potential of tool use but also allows different thought processes for different math problems. As a result, the MAmmoTH series substantially outperform existing open-...
上个月我们发布了我们的第一个数学模型Qwen2-Math,这次我们在Qwen2.5的基础语言模型上构建了Qwen2.5-Math,并继续在推理方面进行研究,包括CoT和Tool Integrated Reasoning。更重要的是,这个模型现在支持英文和中文!Qwen2.5-Math比Qwen2-Math好得多,可能是您数学LLM的最佳选择! 最后,如果您对我们的Qwen2-VL-72B感到满...
As a reference we use the output of the Equation Wizard – the efficient rule-based verbalization tool designed and developed by the authors in previous works. Our experiments are performed with the use of ChatGPT 3.5 – a popular, free-of charge LLM that is frequently and eagerly used by ...
研究人员首先设计了推理的交错格式(interleaving format),为来自GSM8k和MATH数据集的数学问题策划相应的交互式工具使用轨迹(interactive tool-use trajectories),然后在高质量注释(high-quality annotations)上应用模仿学习(imitation learning),从而取得比任何现有的开源模型更好的性能。
研究人员首先设计了推理的交错格式(interleaving format),为来自GSM8k和MATH数据集的数学问题策划相应的交互式工具使用轨迹(interactive tool-use trajectories),然后在高质量注释(high-quality annotations)上应用模仿学习(imitation learning),从而取得比任何现有的开源模型更好的性能。
收集交互式工具使用轨迹(INTERACTIVE TOOL-USE TRAJECTORIES) 现有的数学推理数据集主要包含自然语言或代码中的注释,由于缺少交互式工具使用注释,这为训练工具集成智能体带来了挑战。 为了解决这个问题,研究团队使用GPT-4在GSM8k和MATH训练集上合成高质量的轨迹。