ypwang61/One-Shot-RLVR-Qwen2.5-7B-1.2k-dsr-sub
ypwang61/One-Shot-RLVR-Qwen2.5-7B-1.2k-dsr-sub is a 7.6 billion parameter language model based on the Qwen2.5 architecture, featuring a 32,768 token context length. This model is specifically designed for enhanced reasoning capabilities in large language models, utilizing a novel one-shot reinforcement learning approach. Its primary differentiator is its optimization for reasoning tasks with minimal training examples, making it suitable for applications requiring efficient reasoning performance.
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Overview
ypwang61/One-Shot-RLVR-Qwen2.5-7B-1.2k-dsr-sub is a 7.6 billion parameter language model built upon the Qwen2.5 architecture, featuring a substantial context length of 32,768 tokens. This model's core innovation lies in its application of Reinforcement Learning for Reasoning (RLVR), specifically optimized for scenarios requiring one training example.
Key Capabilities
- Enhanced Reasoning: Designed to improve the reasoning abilities of large language models.
- One-Shot Learning: Leverages a unique reinforcement learning approach that requires only a single training example for effective reasoning.
- Qwen2.5 Foundation: Benefits from the robust architecture and performance characteristics of the Qwen2.5 base model.
- Extended Context Window: Supports processing long sequences with its 32,768 token context length.
Good For
- Research in LLM Reasoning: Ideal for researchers exploring novel methods for improving reasoning with minimal data.
- Applications with Limited Data: Suitable for use cases where extensive fine-tuning data for reasoning tasks is scarce.
- Efficient Reasoning Tasks: Can be applied in scenarios demanding strong reasoning performance without large-scale supervised training.
Further details on the methodology can be found in the associated research paper: Reinforcement Learning for Reasoning in Large Language Models with One Training Example. The project's code is available on GitHub.