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
Website: Project Page
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 in the main GitHub repo.