| --- |
| base_model: |
| - moojink/openvla-7b-oft-finetuned-libero-spatial |
| - moojink/openvla-7b-oft-finetuned-libero-10 |
| - moojink/openvla-7b-oft-finetuned-libero-object |
| - moojink/openvla-7b-oft-finetuned-libero-goal |
| datasets: |
| - yifengzhu-hf/LIBERO-datasets |
| pipeline_tag: robotics |
| license: mit |
| --- |
| |
| # 💪 RIPT-VLA: Interactive Post-Training for Vision-Language-Action Models (arxiv.org/abs/2505.17016) |
|
|
| **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. |
