resistz/RLCR-Calibrated-Qwen3-4B-DAPO-Math14k-3Epoch-330Step
The resistz/RLCR-Calibrated-Qwen3-4B-DAPO-Math14k-3Epoch-330Step model is a 4 billion parameter Qwen3 variant, specifically trained using RLCR on the DAPO-Math14k dataset. This model is optimized for mathematical reasoning tasks, leveraging its specialized training for enhanced performance in this domain. It is designed for applications requiring robust mathematical problem-solving capabilities.
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Model Overview
This model, resistz/RLCR-Calibrated-Qwen3-4B-DAPO-Math14k-3Epoch-330Step, is a 4 billion parameter variant of the Qwen3 architecture. It has been specifically fine-tuned using the RLCR (Reinforcement Learning with Coherence Regularization) method.
Key Capabilities
- Mathematical Reasoning: The model's primary strength lies in its ability to handle mathematical problems, stemming from its training on the DAPO-Math14k dataset.
- Specialized Training: It underwent 3 epochs and 330 steps of training, focusing on improving its mathematical understanding and problem-solving skills.
When to Use This Model
This model is particularly well-suited for use cases that demand strong mathematical reasoning. If your application involves solving complex equations, understanding mathematical concepts, or generating mathematically sound responses, this specialized Qwen3 variant offers a focused solution. Its calibration on a math-specific dataset makes it a strong candidate for tasks where numerical and logical precision in mathematics is paramount.