Update README.md

README.md

@@ -21,6 +21,24 @@ We introduce TianJiangZhuG_3B, an advanced multimodal large language model (MLLM
 
 Key Enhancements:
 
+1. Meticulous Construction of High-Quality Chain-of-Thought (CoT) Datasets
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+   Scale and Coverage: We have systematically built thousands of high-quality Chinese and English reasoning data across multiple domains such as mathematical applications, logical reasoning, and symbolic operations. This ensures the model’s generalization ability in diverse scenarios.
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+   Data Generation Method: Based on selected image-text question-answer pairs, combined with the "Super Chain-of-Thought Model", we automatically generate Chain-of-Thought annotated data containing detailed reasoning paths. This method effectively enhances the model’s step-by-step reasoning and logical coherence
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+3. Multi-Stage GRPO Training Algorithm
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+    Progressive Learning Mechanism: We innovatively propose a multi-stage GRPO (Gradient-based Reward Policy Optimization) training process. Through task design that progresses from shallow to deep and from simple to complex, we guide the model to achieve stepwise capability evolution:
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+       Primary Stage: Focus on judgment and classification tasks to strengthen the model’s understanding of problem structures and basic logic.
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+       Intermediate Stage: Introduce multiple-choice and matching questions to improve the model’s ability to identify key information among distractors.
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+       Advanced Stage: Expand to open-ended generation tasks to encourage the model to conduct free deduction and complete logical expression.
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+    Algorithm Advantages: This training strategy effectively reduces the model’s learning difficulty in complex tasks, improves training stability and strategy convergence efficiency, while significantly enhancing the model’s adaptability across different task types.
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 Evaluation: