Update README training codebase info

README.md

@@ -29,7 +29,7 @@ library_name: transformers
 
 ## 1. Introduction 
 
-**Step 3.5 Flash** ([visit website](https://static.stepfun.com/blog/step-3.5-flash/)) is our most capable open-source foundation model, engineered to deliver frontier reasoning and agentic capabilities with exceptional efficiency. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. This "intelligence density" allows it to rival the reasoning depth of top-tier proprietary models, while maintaining the agility required for real-time interaction.
+**Step 3.5 Flash** ([visit website](https://static.stepfun.com/blog/step-3.5-flash/)) is our most capable open-source foundation model, engineered to deliver frontier reasoning and agentic capabilities with exceptional efficiency. We also open-sourced the training codebase, with support for continue pretrain, SFT, RL (WIP), and evaluation (WIP), and will open-source the SFT data. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. This "intelligence density" allows it to rival the reasoning depth of top-tier proprietary models, while maintaining the agility required for real-time interaction.
 
 ## 2. Key Capabilities
 
@@ -113,6 +113,10 @@ Unlike traditional dense models, Step 3.5 Flash uses a fine-grained routing stra
 
 To improve inference speed, we utilize a specialized MTP Head consisting of a sliding-window attention mechanism and a dense Feed-Forward Network (FFN). This module predicts 4 tokens simultaneously in a single forward pass, significantly accelerating inference without degrading quality.
 
+## 5. Training Codebase
+
+The training codebase for Step 3.5 Flash is available at [SteptronOss](https://github.com/stepfun-ai/SteptronOss).
+
 
 ## 📜 Citation
 
@@ -131,4 +135,4 @@ If you find this project useful in your research, please cite our technical repo
 ```
 
 ## License
-This project is open-sourced under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+This project is open-sourced under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).