RLDX-1-MT-ALLEX

RLDX-1-MT-ALLEX

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<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-MT-ALLEX — RLDX-1 mid-trained on the ALLEX humanoid dataset with every optional MSAT module enabled: memory, motion, physics (tactile + torque), and video. It is the most feature-complete checkpoint in the family and the recommended initialization for tasks that need long-horizon memory or rich physical signal conditioning.

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).

Quick start

Installation

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

Inference

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

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

action = policy.get_action(observation)

Real-time serving (ZeroMQ)

uv run python rldx/eval/run_rldx_server.py \
    --model-path RLWRLD/RLDX-1-MT-ALLEX \
    --embodiment-tag GENERAL_EMBODIMENT \
    --host 0.0.0.0 --port 20000

Finetune from this checkpoint (preserve all add-ons)

To keep memory / motion / physics active during downstream finetuning, mirror the original training flags. See docs/training.md for the full recipe.

Model details

• Architecture: Multi-Stream Action Transformer (MSAT) policy on a Qwen3-VL backbone, with memory + motion + physics + video modules all enabled. Trained with flow matching.
• Inputs: RGB video (4 frames), state proprioception, tactile and torque streams, language instruction.
• Outputs: Action chunks of length 16, plus auxiliary flow-matching predictions of future tactile / torque signals.
• Embodiment tag: GENERAL_EMBODIMENT.
• Base model: RLWRLD/RLDX-1-PT.
• Backbone: Qwen/Qwen3-VL-8B-Instruct.
• Mid-train data: ALLEX dataset.
• Params: 8.1B.

For the full architectural walkthrough including how memory / motion / physics modules are wired, see docs/architecture.md.

RLDX-1 model family

Intended use & limitations

Intended use. Research on memory- and physical-signal-conditioned robotic manipulation, mid-train initialization for downstream tasks that benefit from history or tactile/torque conditioning, 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 §3.5 for the full list.

Limitations. Active memory / motion / physics modules add inference cost and require feeding tactile + torque observations at inference time to fully exploit the physics stream. If the deployment hardware lacks tactile or torque sensors, prefer RLDX-1-PT or RLDX-1-MT-DROID. Conditioning is biased toward the ALLEX humanoid embodiment; for fundamentally different morphologies, finetune.

Citation

@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 for the full text. By using this model you agree to those terms, including the use restrictions in §3.5.