Update README.md

README.md

@@ -35,9 +35,9 @@ For full transparency and reproducibility, please refer to our technical report
 
 
 
-## Model Details
+## Model Highlights
 
-JT-Math-8B-Thinking achieves its cutting-edge performance on complex mathematical challenges through a rigorous, multi-stage training methodology. Starting with the robust JT-Math-8B-Base model, our pipeline first implemented Supervised Fine-Tuning (SFT). This involved training on a high-quality, bilingual dataset of intricate math problems, capitalizing on the model's impressive native 32,768-token context window. Subsequently, an advanced Reinforcement Learning (RL) phase, incorporating a multi-stage curriculum of progressively harder problems, further honed its reasoning abilities.
+JT-Math-8B-Thinking achieves its cutting-edge performance on complex mathematical challenges through a rigorous, multi-stage training methodology. Starting with the robust JT-Math-8B-Base model, our pipeline first implemented Supervised Fine-Tuning (SFT). This involved training on a high-quality, bilingual dataset of intricate math problems with 32,768-token context window. Subsequently, an advanced Reinforcement Learning (RL) phase, incorporating a multi-stage curriculum of progressively harder problems, further honed its reasoning abilities.