RLDX-1 Technical Report
Authors: name: Dongyoung Kim, name: Huiwon Jang, name: Myungkyu Koo, name: Suhyeok Jang, name: Taeyoung Kim, name: Beomjun Kim, name: Byungjun Yoon, name: Changsung Jang, name: Daewon Choi, name: Dongsu Han, name: Donguk Lee, name: Heeseung Kwon, name: Hojin Jeon, name: Jaehyun Kang, name: Jaekyoung Bae, name: Jihyuk Lee, name: Jimin Lee, name: John Won, name: Joonwoo Ahn, name: Junhyeong Park, name: Junyoung Sung, name: Kyungmin Lee, name: Minseong Han, name: Minsung Yoon, name: Sejune Joo, name: Seonil Son, name: Seungcheol Park, name: Seunggeun Cho, name: Seungjun Moon, name: Seungku Kim, name: Yonghoon Dong, name: Yongjin Cho, name: Youngchan Kim, name: Chang Hwan Kim, name: Dohyeon Kim, name: Hazel Lee, name: Heecheol Kim, name: Hensen Ahn, name: Hyungkyu Ryu, name: Hyunsoo Choi, name: Hyunsoo Shin, name: Jaeheon Jung, name: Jaewoo Kim, name: Jinwook Kim, name: Joochul Chang, name: Joonsoo Kim, name: Junghun Park, name: Jungwoo Park, name: Junho Cho, name: Junhyeok Park, name: Junwon Lee, name: Kangwook Lee, name: Kwanghoon Kim, name: Kyoungwhan Choe, name: Manoj Bhadu, name: Nayoung Oh, name: Sangjun Kim, name: Sangwoo Kim, name: Seunghoon Shim, name: Seunghyun Kim, name: Seungjun Lee, name: Seungyup Ka, name: Sungryol Yang, name: Wook Jung, name: Yashu Shukla, name: Yeonjae Lee, name: Yeonwoo Bae, name: Jinwoo Shin
Organization: RLWRLD

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, memory-aware decision making, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including synthesizing training data for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. π_{0.5} and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while π_{0.5} and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation.