ypwang61/One-Shot-RLVR-Qwen2.5-Math-1.5B-pi13
The ypwang61/One-Shot-RLVR-Qwen2.5-Math-1.5B-pi13 model is a 1.5 billion parameter language model based on the Qwen2.5 architecture, fine-tuned for mathematical reasoning tasks. It utilizes Reinforcement Learning for Reasoning (RLVR) with a novel one-shot training approach, enabling efficient learning from a single example. This model is specifically designed to enhance mathematical problem-solving capabilities in LLMs, offering a specialized solution for complex numerical and logical challenges within its 32768-token context window.
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Overview
The ypwang61/One-Shot-RLVR-Qwen2.5-Math-1.5B-pi13 is a specialized 1.5 billion parameter language model built upon the Qwen2.5 architecture. Its core innovation lies in its training methodology: Reinforcement Learning for Reasoning (RLVR), applied with a unique one-shot training example approach. This allows the model to learn complex reasoning patterns efficiently from minimal data.
Key Capabilities
- Enhanced Mathematical Reasoning: Specifically fine-tuned to excel at mathematical problem-solving, including arithmetic, algebra, and logical deduction.
- Efficient Learning: Leverages a one-shot RLVR technique, demonstrating the potential for effective reasoning skill acquisition with limited training data.
- Qwen2.5 Foundation: Benefits from the robust base architecture of Qwen2.5, providing a strong linguistic and general knowledge foundation.
- Long Context Window: Supports a context length of 32768 tokens, enabling the processing of lengthy mathematical problems or complex reasoning chains.
Good For
- Mathematical Problem Solving: Ideal for applications requiring accurate and robust solutions to mathematical questions.
- Research in Efficient RL: Serves as a practical example and baseline for research into data-efficient reinforcement learning for reasoning tasks.
- Specialized AI Assistants: Suitable for integration into AI systems focused on STEM education, scientific computing, or data analysis where mathematical accuracy is paramount.
- Exploring One-Shot Learning: Useful for developers and researchers interested in the capabilities and limitations of models trained with very few examples for complex tasks.
For more technical details and the underlying research, refer to the associated paper: Reinforcement Learning for Reasoning in Large Language Models with One Training Example.