Add GitHub repo link

README.md

@@ -10,6 +10,8 @@ tags:
 
 # Self-Grounded Prediction (policy_training/) — released checkpoint
 
+[![GitHub](https://img.shields.io/badge/GitHub-CGuangyan--BIT%2FHOST-181717?logo=github)](https://github.com/CGuangyan-BIT/HOST)
+
 This module implements **self-grounded prediction**, the mechanism behind **HOST (Human-to-robot
 One-shot Skill Transfer)** that resolves execution from a coupled visual demonstration: it
 localizes the robot's current progress within a visual demonstration, predicts the robot's own
@@ -18,8 +20,9 @@ predicted future. This is implemented as a single autoregressive diffusion model
 video/action experts (a Mixture-of-Transformers architecture) built on top of the
 [Fast-WAM](https://arxiv.org/abs/2603.16666) codebase — see [Acknowledgements](#acknowledgements).
 
-This is a companion checkpoint for the `policy_training/` module of the HOST code release; see
-that module's own `README.md` / `README_zh.md` for the full pipeline (`data_preprocessing/` →
+This is a companion checkpoint for the `policy_training/` module of the
+[HOST code release](https://github.com/CGuangyan-BIT/HOST); see that repo's
+`policy_training/README.md` / `README_zh.md` for the full pipeline (`data_preprocessing/` →
 `alignment/` → `coupling/` → `policy_training/`).
 
 **Training regime: robot→robot, not human→robot.** This checkpoint's task-video conditioning