The question behind the project
Reasoning performance is often associated with model scale. Our work asks whether targeted post-training and greater diversity in optimization data can help a much smaller model acquire stronger reasoning behavior.
Why this is worth sharing
Beyond the benchmark results, the project reflects a broader research direction: capability gains do not have to come only from increasing parameter count. Data design and post-training strategy can be equally important parts of the system.
Paper and code
The paper and open-source implementation contain the complete method, experiments, and evaluation.