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README.md

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+# 💪 RIPT-VLA: Reinforcement Interactive Post-Training for Vision-Language-Action Models
+
+**Authors**: Shuhan Tan, Kairan Dou, Yue Zhao, Philipp Krähenbühl  
+**Codebase**: [GitHub – RIPT-VLA](https://github.com/Ariostgx/ript-vla)  
+**Website**: [Project Page](https://ariostgx.github.io/ript_vla/)  
+
+> **RIPT-VLA** enables interactive post-training for any pretrained Vision-Language-Action (VLA) model using only **sparse binary success rewards**.  
+> With **K-rollout interaction**, **dynamic sampling**, and **leave-one-out advantage estimation**, RIPT-VLA achieves **state-of-the-art** performance in extremely low-data regimes.
+
+---
+
+## 🧠 Model Summary
+
+RIPT-VLA takes a pretrained VLA model (e.g., QueST or OpenVLA-OFT) and improves its performance by fine-tuning it with reinforcement learning based on success/failure signals only — no dense rewards or value functions required.
+
+Supported models:
+- ✅ QueST (small, efficient)
+- ✅ OpenVLA-OFT (large-scale, high-capacity)
+
+---
+
+## 🧪 Model Use
+
+### ✅ Intended Use
+
+- Research on post-training VLA models via RL
+- Evaluation on LIBERO benchmarks (LIBERO-90, Goal, Object, Spatial, Long)
+- Studying low-data reinforcement learning settings
+
+---
+
+## 📦 Checkpoints
+
+All checkpoints are hosted here in this repository.
+
+### ✔️ QueST Checkpoints
+
+| Suite            | SFT Checkpoint | RIPT Checkpoint |
+|------------------|----------------|-----------------|
+| LIBERO-90        | ✅              | ✅              |
+| LIBERO-GOAL      | ✅              | ✅              |
+| LIBERO-LONG      | ✅              | ✅              |
+| LIBERO-OBJECT    | ✅              | ✅              |
+| LIBERO-SPATIAL   | ✅              | ✅              |
+
+Each QueST checkpoint is ~80MB.
+
+### ✔️ OpenVLA-OFT Checkpoints
+
+| Suite            | SFT Scale Head | RIPT LoRA Adaptor |
+|------------------|----------------|--------------------|
+| LIBERO-GOAL      | ✅              | ✅    |
+| LIBERO-LONG      | ✅              | ✅                 |
+| LIBERO-OBJECT    | ✅              | ✅                 |
+| LIBERO-SPATIAL   | ✅              | ✅                 |
+
+OpenVLA-OFT scale heads are ~300MB; RIPT LoRA adaptors are ~1GB.
+
+---
+
+## 🛠 How to Use
+
+For usage, see [INSTALL.md](https://github.com/Ariostgx/ript-vla/blob/main/INSTALL.md) in the main GitHub repo.