README.md

---
license: other
license_name: rlwrld-model-license-v1.0
license_link: LICENSE.md
library_name: transformers
pipeline_tag: robotics
tags:
  - robotics
  - vla
  - vision-language-action
  - manipulation
  - flow-matching
  - rldx
  - libero
base_model: RLWRLD/RLDX-1-PT
---

# RLDX-1-FT-LIBERO

[Paper](https://arxiv.org/abs/2605.03269)  ·  [Project page](https://rlwrld.ai/rldx-1)  ·  [Code](https://github.com/RLWRLD/RLDX-1)  ·  [Models](https://huggingface.co/collections/RLWRLD/rldx-1)

<p align="center">
<img src="teaser.png" width="100%" alt="RLDX-1 teaser">
</p>

**RLDX-1** is a general-purpose Robot Foundation Model designed for dexterous
manipulation. Powered by a **Multi-Stream Action Transformer (MSAT)**, it
seamlessly unifies multimodal perception (visual + tactile), high-DoF
actuation, and memory-aware decision-making in a single architecture.

This repository hosts **`RLDX-1-FT-LIBERO`** — RLDX-1 finetuned on the
**LIBERO 4-task suite (goal, object, spatial, long)**. The same checkpoint is also evaluated on the
**LIBERO-Plus** generalization suite. It achieves **97.8%** on LIBERO (Avg)
and **87.6%** on LIBERO-Plus.

## Highlights

- **Multi-Stream Action Transformer (MSAT).** Cognition, physics, and
  action each get a dedicated stream coupled by joint self-attention —
  an extension of MM-DiT to action modeling.
- **Motion awareness.** Multi-frame observations + a motion module
  capture temporal dynamics; intermediate VLM layers compress video
  tokens to keep the policy efficient.
- **Long-term memory.** A memory module fuses past cognition features
  with the current ones for history-grounded decisions beyond a short
  multi-frame window.
- **Physical sensing.** Tactile and torque enter as a dedicated physics
  stream; the decoder is jointly trained to predict future physical
  signals.
- **Three-stage training.** Pre-training (generalization) → mid-training
  (functionality) → post-training (task adaptation), with synthetic data
  augmenting rare manipulation scenarios.
- **Real-time inference.** Static graph capture + custom fused kernels
  bring the all-modality model to **43.7 ms / step on RTX 5090
  (1.63× speedup, >22 Hz)**.

## Performance

| Benchmark | Success Rate |
|---|---|
| LIBERO (Avg) | **97.8%** |
| LIBERO-Plus | **87.6%** |

## Quick start

### Installation

```bash
git clone https://github.com/RLWRLD/RLDX-1.git
cd RLDX
uv sync --python 3.10
uv pip install -e .
```

### Inference

```python
from rldx.policy.rldx_policy import RLDXPolicy
from rldx.data.embodiment_tags import EmbodimentTag

policy = RLDXPolicy(
    model_path="RLWRLD/RLDX-1-FT-LIBERO",
    embodiment_tag=EmbodimentTag.GENERAL_EMBODIMENT,
    device="cuda:0",
)

action = policy.get_action(observation)
```

### Real-time serving (ZeroMQ)

```bash
uv run python rldx/eval/run_rldx_server.py \
    --model-path RLWRLD/RLDX-1-FT-LIBERO \
    --embodiment-tag GENERAL_EMBODIMENT \
    --host 0.0.0.0 --port 20000
```

To reproduce the benchmark numbers end-to-end:

- LIBERO: [`run_scripts/eval/libero/README.md`](https://github.com/RLWRLD/RLDX-1/blob/main/run_scripts/eval/libero/README.md)
- LIBERO-Plus: [`run_scripts/eval/libero_plus/README.md`](https://github.com/RLWRLD/RLDX-1/blob/main/run_scripts/eval/libero_plus/README.md)

## Model details

- **Architecture:** Multi-Stream Action Transformer (MSAT) policy on a
  Qwen3-VL backbone with cognition-token perceptual summary. Trained with
  flow matching.
- **Inputs:** RGB video (default 4 frames), state proprioception, language
  instruction.
- **Outputs:** Action chunks of length 16.
- **Embodiment tag:** `GENERAL_EMBODIMENT`.
- **Base model:** [`RLWRLD/RLDX-1-PT`](https://huggingface.co/RLWRLD/RLDX-1-PT).
- **Backbone:** [`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct).
- **Finetune data:** LIBERO 4-task suite (goal, object, spatial, long).
- **Params:** 6.9B.

For the full architectural walkthrough see
[`docs/architecture.md`](https://github.com/RLWRLD/RLDX-1/blob/main/docs/architecture.md).

## RLDX-1 model family

| Checkpoint | Description |
|---|---|
| [`RLDX-1-PT`](https://huggingface.co/RLWRLD/RLDX-1-PT) | Multi-source pretrained foundation |
| [`RLDX-1-VLM`](https://huggingface.co/RLWRLD/RLDX-1-VLM) | Qwen3-VL-8B vision-language backbone |
| [`RLDX-1-FT-ROBOCASA`](https://huggingface.co/RLWRLD/RLDX-1-FT-ROBOCASA) | RoboCasa Kitchen 24-task finetune |
| [`RLDX-1-FT-RC365`](https://huggingface.co/RLWRLD/RLDX-1-FT-RC365) | RoboCasa-365 cross-task finetune |
| [`RLDX-1-FT-LIBERO`](https://huggingface.co/RLWRLD/RLDX-1-FT-LIBERO) | LIBERO 4-task suite (goal, object, spatial, long) finetune (this repo) |
| [`RLDX-1-FT-SIMPLER-GOOGLE`](https://huggingface.co/RLWRLD/RLDX-1-FT-SIMPLER-GOOGLE) | SIMPLER Google VM/VA finetune |
| [`RLDX-1-FT-SIMPLER-WIDOWX`](https://huggingface.co/RLWRLD/RLDX-1-FT-SIMPLER-WIDOWX) | SIMPLER WidowX finetune |
| [`RLDX-1-FT-GR1`](https://huggingface.co/RLWRLD/RLDX-1-FT-GR1) | GR-1 Tabletop finetune |
| [`RLDX-1-MT-DROID`](https://huggingface.co/RLWRLD/RLDX-1-MT-DROID) | DROID mid-train |
| [`RLDX-1-MT-ALLEX`](https://huggingface.co/RLWRLD/RLDX-1-MT-ALLEX) | All add-ons (memory + motion + physics + video) |

## Intended use & limitations

**Intended use.** Research on robotic manipulation, simulation benchmarking
on LIBERO and LIBERO-Plus, and non-commercial real-robot deployment under
the conditions of the RLWRLD Model License v1.0.

**Out of scope.** Commercial deployment, military or weapons applications,
non-consensual surveillance, and any use that violates applicable laws or
regulations. See [`LICENSE.md`](LICENSE.md) §3.5 for the full list.

**Limitations.** Performance is reported on the LIBERO 4-task suite (goal, object, spatial, long) training
distribution and the LIBERO-Plus generalization suite. Out-of-distribution
scenes, novel objects, or non-Franka embodiments are not guaranteed. For
other embodiments or datasets, finetune from
[`RLDX-1-PT`](https://huggingface.co/RLWRLD/RLDX-1-PT) instead.

## Citation

```bibtex
@article{rldx2026,
  title={RLDX-1 Technical Report},
  author={Kim, Dongyoung and Jang, Huiwon and Koo, Myungkyu and Jang, Suhyeok and Kim, Taeyoung and others},
  year={2026},
  note={RLWRLD},
  eprint={2605.03269},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.03269}
}
```

## License

Released under the **RLWRLD Model License v1.0** — a non-commercial license
with attribution and share-alike requirements. See [`LICENSE.md`](LICENSE.md) for
the full text. By using this model you agree to those terms, including the
use restrictions in §3.5.