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arxiv: 2605.03269 · v2 · submitted 2026-05-05 · 💻 cs.RO · cs.AI· cs.LG

Recognition: 2 theorem links

RLDX-1 Technical Report

Dongyoung Kim , Huiwon Jang , Myungkyu Koo , Suhyeok Jang , Taeyoung Kim , Beomjun Kim , Byungjun Yoon , Changsung Jang
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Daewon Choi Dongsu Han Donguk Lee Heeseung Kwon Hojin Jeon Jaehyun Kang Jaekyoung Bae Jihyuk Lee Jimin Lee John Won Joonwoo Ahn Junhyeong Park Junyoung Sung Kyungmin Lee Minseong Han Minsung Yoon Sejune Joo Seonil Son Seungcheol Park Seunggeun Cho Seungjun Moon Seungku Kim Yonghoon Dong Yongjin Cho Youngchan Kim Chang Hwan Kim Dohyeon Kim Hazel Lee Heecheol Kim Hensen Ahn Hyungkyu Ryu Hyunsoo Choi Hyunsoo Shin Jaeheon Jung Jaewoo Kim Jinwook Kim Joochul Chang Joonsoo Kim Junghun Park Jungwoo Park Junho Cho Junhyeok Park Junwon Lee Kangwook Lee Kwanghoon Kim Kyoungwhan Choe Manoj Bhadu Nayoung Oh Sangjun Kim Sangwoo Kim Seunghoon Shim Seunghyun Kim Seungjun Lee Seungyup Ka Sungryol Yang Wook Jung Yashu Shukla Yeonjae Lee Yeonwoo Bae Jinwoo Shin
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Pith reviewed 2026-05-08 18:56 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.LG
keywords vision-language-action modelsdexterous manipulationhumanoid robotsmulti-stream transformerrobotic policiesreal-world tasksALLEX benchmark
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The pith

RLDX-1 uses a multi-stream transformer to reach 86.8% success on ALLEX humanoid tasks while other VLAs stay near 40%.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces RLDX-1 as a general-purpose robotic policy for dexterous manipulation. It builds this policy on the Multi-Stream Action Transformer architecture that processes vision, language, and action through separate streams before applying cross-modal joint self-attention. The approach adds functional capabilities such as motion awareness, long-term memory, and physical sensing that standard vision-language-action models still lack. Evaluations show RLDX-1 outperforms recent models including π0.5 and GR00T N1.6 on both simulation benchmarks and real-world tasks, with the largest gap appearing on high-DoF humanoid control.

Core claim

RLDX-1 is a robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer that integrates heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. Combined with data synthesis for rare scenarios, specialized learning procedures for human-like manipulation, and inference optimizations, the policy achieves higher success rates than frontier VLAs across simulation and real-world settings. On ALLEX humanoid tasks it reaches 86.8% success while π0.5 and GR00T N1.6 reach around 40%, showing effective control of high-DoF robots under diverse functional demands.

What carries the argument

The Multi-Stream Action Transformer (MSAT), which unifies modalities by running each in its own stream and then applying joint self-attention across streams to combine scene understanding with action generation.

If this is right

  • RLDX-1 can control high-DoF humanoid robots reliably across contact-rich and dynamic tasks.
  • Data synthesis for rare manipulation scenarios improves coverage of edge cases that general pre-training misses.
  • Specialized learning procedures and real-time inference optimizations make the policy practical for physical robot deployment.
  • Vision-language-action models can be extended to support broader functional capabilities without losing general versatility.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A multi-stream design might reduce reliance on ever-larger unified pre-training by letting each modality develop its own representations first.
  • The same architecture could be tested on non-humanoid platforms to check whether the performance lift depends on the specific robot morphology.
  • Future comparisons could isolate whether cross-modal joint self-attention or the separate streams contribute most to the observed gains.

Load-bearing premise

The performance differences arise from the MSAT architecture and listed system-level choices rather than uncontrolled differences in training data volume, evaluation protocols, robot hardware calibration, or task definitions.

What would settle it

A side-by-side retraining and evaluation of RLDX-1, π0.5, and GR00T N1.6 on identical data volumes, identical task definitions, and identical hardware calibration to test whether the success-rate gap remains.

Figures

Figures reproduced from arXiv: 2605.03269 by Beomjun Kim, Byungjun Yoon, Chang Hwan Kim, Changsung Jang, Daewon Choi, Dohyeon Kim, Dongsu Han, Donguk Lee, Dongyoung Kim, Hazel Lee, Heecheol Kim, Heeseung Kwon, Hensen Ahn, Hojin Jeon, Huiwon Jang, Hyungkyu Ryu, Hyunsoo Choi, Hyunsoo Shin, Jaeheon Jung, Jaehyun Kang, Jaekyoung Bae, Jaewoo Kim, Jihyuk Lee, Jimin Lee, Jinwook Kim, Jinwoo Shin, John Won, Joochul Chang, Joonsoo Kim, Joonwoo Ahn, Junghun Park, Jungwoo Park, Junho Cho, Junhyeok Park, Junhyeong Park, Junwon Lee, Junyoung Sung, Kangwook Lee, Kwanghoon Kim, Kyoungwhan Choe, Kyungmin Lee, Manoj Bhadu, Minseong Han, Minsung Yoon, Myungkyu Koo, Nayoung Oh, Sangjun Kim, Sangwoo Kim, Sejune Joo, Seonil Son, Seungcheol Park, Seunggeun Cho, Seunghoon Shim, Seunghyun Kim, Seungjun Lee, Seungjun Moon, Seungku Kim, Seungyup Ka, Suhyeok Jang, Sungryol Yang, Taeyoung Kim, Wook Jung, Yashu Shukla, Yeonjae Lee, Yeonwoo Bae, Yonghoon Dong, Yongjin Cho, Youngchan Kim.

Figure 1
Figure 1. Figure 1: Overview of RLDX-1. RLDX-1 is a Vision-Language-Action model (VLA) that integrates diverse functional capabilities for dexterous manipulation in real-world deployment. All project leads contributed equally, and authors with equal contribution are listed alphabetically by first name. Names without additional markers indicate core contributors and additional contributors are listed in Appendix A. 1 arXiv:260… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of RLDX-1. Given video observations and a language instruction, RLDX-1 predicts future actions through three key functionalities: motion awareness via the Motion Module, long-term memory via the Memory Module, and physical sensing via the Physics Stream that ingests torque and tactile signals. A VLM backbone grounds vision and language into a cognition representation, which is jointly denoised wit… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the RLDX-1 architecture. RLDX-1 consists of two main components: a Vision-Language Model (VLM) and an action model. The VLM takes video observations as input, captures motion-aware visual-language repre￾sentations, and converts the extracted representations into cognition features that are passed to the action model. These cognition features are further augmented with memory features through a … view at source ↗
Figure 4
Figure 4. Figure 4: Overview of the synthetic data framework. (1) Data Generation: a source demonstration is diversified via scene and task augmentation, and an inverse dynamics model (IDM) annotates action labels for the generated videos. (2) Data Filtering: IDM-predicted actions are replayed in a simulator, and a motion-consistency classifier compares the rollout against the synthetic video to retain only consistent samples… view at source ↗
Figure 5
Figure 5. Figure 5: Examples of synthetic data. We visualize one example of our synthetic data: (a) original in-house ALLEX stack cup noodles demonstration, (b) task-augmented variant with a VLM-generated instruction, and (c) scene-augmented variant via I2I editing of the initial frame followed by I2V generation. Task Augmentation Task augmentation synthesizes executable task instructions conditioned on an initial scene frame… view at source ↗
Figure 6
Figure 6. Figure 6: Overview of dataset composition for pre-training RLDX-1. RLDX-1 pre-train dataset covers multiple embodi￾ments spanning single-arm grippers, dual-arm grippers, and humanoid platforms equipped with dexterous hands, including synthetic GR-1 humanoid data (Section 3.3). Motion-Consistency Filtering While video quality filtering removes visually implausible or instruction-inconsistent videos, the IDM-predicted… view at source ↗
Figure 7
Figure 7. Figure 7: Overview of data compositions of RLDX-1 mid-training. The mid-training data covers two target platforms: ALLEX and Franka Research 3 platform (FR3). The ALLEX composition combines in-house teleoperation data with synthetic data from our generation pipeline (Section 3.3), while the FR3 composition combines in-house teleoperation data with the public DROID dataset (Khazatsky et al., 2024). Implementation Det… view at source ↗
Figure 8
Figure 8. Figure 8: Normalized value over timesteps for a cube pick-and-place task. The text prediction critic (Ours) better reflects the task progress than the distributional critic: (a) It produces more monotonically increasing values for episodes that succeed on the first attempt, and (b) captures both failure and recovery in episodes that succeed after a retry. 4.3. Post-Training Imitation learning (IL) on embodiment- and… view at source ↗
Figure 9
Figure 9. Figure 9: Dynamic graph vs. Static graph (Ours). Dynamic graph execution accumulates launch overhead across repeated graph launches. Static graph conversion captures the forward pass as a single CUDA Graph, reducing launch overhead. advantage-conditioned supervision derived from the critic. After that, we iteratively improve both components: at each iteration, we roll out the current policy to collect additional tra… view at source ↗
Figure 10
Figure 10. Figure 10: Effect of operator fusion on memory access. (a) Without fusion, each kernel writes its output to memory and the next kernel reads it back, and these memory round-trips dominate the runtime. (b) With fusion, the operators access memory only once for the input load and once for the output store, minimizing memory traffic. Graph Fragmentation The remaining launch overhead is caused by graph fragmentation dur… view at source ↗
Figure 11
Figure 11. Figure 11: Overview of the simulation benchmarks. (a) We consider established benchmarks, including LIBERO (Liu et al., 2023), SIMPLER (Li et al., 2024b) with Google Robot and WidowX for evaluating RLDX-1 on a single-arm robot, and consider LIBERO-Plus (Fei et al., 2025), SIMPLER Google-VA for evaluating robustness to diverse variations. (b) We further consider more challenging benchmarks, including RoboCasa Kitchen… view at source ↗
Figure 12
Figure 12. Figure 12: Real-robot platforms. We use (a) OpenArm with Inspire RH56F1 Hands, a 28-DoF upper-body humanoid with stereo egocentric cameras; (b) ALLEX, a 48-DoF upper-body humanoid with stereo egocentric cameras; (c) Franka Research 3 platform (FR3), a 7-DoF single-arm robot with an AnySkin tactile sensor, wrist and third-person cameras. The advantage of RLDX-1 becomes more pronounced on challenging benchmarks. On Ro… view at source ↗
Figure 13
Figure 13. Figure 13: OpenArm humanoid benchmark. We visualize the initial setup for six tasks for evaluating versatility in humanoid manipulation: Basic PnP, Directional PnP (Shelf), Directional PnP (Dish Rack), Unseen Object (Instance), Unseen Task (Placement), and Object Grounding. Basic PnP Directional PnP (shelf) Directional PnP (dish rack) Unseen Object (instance) Unseen Task (placement) Object Grounding 0 20 40 60 80 Su… view at source ↗
Figure 14
Figure 14. Figure 14: OpenArm humanoid benchmark results. We report the success rates (%) of fine-tuned VLAs. RLDX-1 substantially improves performance across all tasks, spanning both seen and unseen settings during training. Evaluation Protocol We conduct each evaluation trial in a three-object tabletop scene consisting of one target object and two distractors, and vary the initial object layout across three predefined config… view at source ↗
Figure 15
Figure 15. Figure 15: ALLEX humanoid benchmark. We design four tasks for evaluating functional capabilities in dexterous humanoid manipulation: Conveyor Pick-and-Place, Object-in-Box Selection, Card Slide-and-Pick, and Pot-to-Cup Pouring. 6.3. Real-World Experiments: ALLEX Humanoid To evaluate the functional capabilities of RLDX-1 in real-world dexterous manipulation, we curate a set of realistic, practical task scenarios for … view at source ↗
Figure 16
Figure 16. Figure 16: ALLEX humanoid benchmark results. We report the success rates (%) of VLAs fine-tuned on the training dataset of each task. RLDX-1 substantially outperforms the baselines across all task categories, including motion awareness (Conveyor Pick-and-Place), long-term memory (Object-in-Box Selection), and physical sensing (Card Slide-and-Pick, Pot-to￾Cup-Pouring). • Pot-to-Cup Pouring. This task evaluates unders… view at source ↗
Figure 17
Figure 17. Figure 17: Franka Research 3 benchmark. We design six tasks for evaluating functional capabilities in dexterous single-arm robot manipulation: Spin Tracking, Pong Game, Cup Swapping, Shell Game, Plug Insertion, and Egg Pick-and-Place. 6.4. Real-World Experiments: Franka Research 3 To further evaluate the same functional capabilities on a different real-robot embodiment, we conduct experiments on the Franka Research … view at source ↗
Figure 18
Figure 18. Figure 18: Franka Research 3 benchmark results. We report the success rates (%) of VLAs fine-tuned on the training dataset of each task. RLDX-1 substantially outperforms the baselines across all task categories, including motion awareness (Spin Tracking, Pong Game), long-term history (Cup Swapping, Shell Game), and physical sensing (Plug Insertion, Egg PnP). Evaluation Protocol We adopt task-specific evaluation crit… view at source ↗
Figure 19
Figure 19. Figure 19: Attention-based analysis. We visualize the self-attention scores from the last prompt token to the image patch tokens during task execution. abstraction for action generation, whereas earlier layers lack sufficient semantics and later layers may become less aligned with fine-grained visual details required for manipulation. Meanwhile, robot-specific VQA training also plays an important role in adapting VL… view at source ↗
Figure 21
Figure 21. Figure 21: Results of RLDX-1 on the Light Bulb Twisting task. We report the box-and-whisker plot by (a) frames and (b) number of attempts, for expert demonstration, RLDX-1 after adopting imitation learning (BC), and reinforcement learning (RECAP1 to RECAP3) view at source ↗
Figure 22
Figure 22. Figure 22: On the offline dataset, we sampled N = 10 from the RECAP3 policy and measured the Q value to see whether test-time sampling would be effective. (a) Picking the best action chunk according to Q-value makes a difference, and (b), (c) the gap becomes clear as temperature increases. (d) Q-value versus frame of an Light Bulb Twisting episode shows that Q improvement by temperature takes place uniformly within … view at source ↗
Figure 23
Figure 23. Figure 23: Test-time sampling result of RL-trained RLDX-1 checkpoints (RECAP view at source ↗
read the original abstract

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, long-term memory, 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 data synthesis 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. $\pi_{0.5}$ 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 $\pi_{0.5}$ 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The paper introduces RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT) architecture that integrates heterogeneous modalities via modality-specific streams and cross-modal attention. It further incorporates system-level choices including data synthesis for rare scenarios, specialized learning procedures, and inference optimizations. The central claim is that RLDX-1 consistently outperforms recent frontier VLAs (e.g., π_{0.5} and GR00T N1.6) on simulation benchmarks and real-world tasks, with a highlighted result of 86.8% success rate on ALLEX humanoid tasks versus approximately 40% for the baselines.

Significance. If the performance differences can be rigorously attributed to the MSAT architecture and listed design choices rather than confounds, this would constitute a notable contribution to vision-language-action models for complex, high-DoF humanoid manipulation requiring functional capabilities beyond general versatility. The architecture's unification of modalities addresses a recognized limitation in current VLAs, but the current lack of supporting evidence prevents assessing whether the result holds.

major comments (3)
  1. Abstract: The claim of consistent outperformance, including the specific 86.8% versus ~40% success rates on ALLEX humanoid tasks, is presented without any description of the experimental setup, baseline implementations, training data volumes or compositions, task definitions, success criteria, or hardware calibration details. This prevents determining whether the gap is attributable to MSAT rather than uncontrolled differences in training regime or evaluation protocols.
  2. Abstract: No ablation studies, controlled experiments, or comparisons isolating the MSAT architecture from the system-level choices (data synthesis, specialized learning procedures, inference optimizations) are reported, leaving the central performance attribution unsupported.
  3. Abstract: The manuscript supplies no information on the number of evaluation trials, error bars, variance, or statistical tests for the reported success rates, which is required to substantiate claims of consistent superiority across benchmarks and real-world tasks.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which identifies key areas where additional clarity and evidence are needed to support our claims. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: Abstract: The claim of consistent outperformance, including the specific 86.8% versus ~40% success rates on ALLEX humanoid tasks, is presented without any description of the experimental setup, baseline implementations, training data volumes or compositions, task definitions, success criteria, or hardware calibration details. This prevents determining whether the gap is attributable to MSAT rather than uncontrolled differences in training regime or evaluation protocols.

    Authors: We agree that the abstract is too concise and omits critical context on the experimental setup, baselines, data composition, task definitions, success criteria, and hardware details. We will revise the abstract to include a brief high-level summary of the evaluation protocol and benchmarks. We will also expand the main text to provide complete descriptions of training data volumes, baseline implementations, task specifications, success criteria, and hardware calibration, enabling readers to assess potential confounds and attribute performance differences more clearly. revision: yes

  2. Referee: Abstract: No ablation studies, controlled experiments, or comparisons isolating the MSAT architecture from the system-level choices (data synthesis, specialized learning procedures, inference optimizations) are reported, leaving the central performance attribution unsupported.

    Authors: The referee correctly notes the absence of ablations or controlled experiments that isolate the MSAT architecture from the system-level choices. While the full RLDX-1 system shows strong results against frontier VLAs, this does not rigorously separate the architectural contribution. We will add a dedicated ablation subsection in the Experiments section, including controlled comparisons (e.g., MSAT versus a standard transformer backbone with other components held fixed) to better support the attribution of gains to the multi-stream design. revision: yes

  3. Referee: Abstract: The manuscript supplies no information on the number of evaluation trials, error bars, variance, or statistical tests for the reported success rates, which is required to substantiate claims of consistent superiority across benchmarks and real-world tasks.

    Authors: We acknowledge that the reported success rates are presented without details on the number of trials, error bars, variance, or statistical tests. In the revised manuscript, we will update all results tables and figures to specify the number of evaluation trials per task, include standard deviations or confidence intervals, and report appropriate statistical tests (such as paired t-tests) to substantiate the consistency of the observed outperformance. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance comparison with no derivations or self-defined reductions

full rationale

The paper is an empirical technical report introducing the RLDX-1 policy and MSAT architecture, then reporting success rates on benchmarks and real-world tasks (e.g., 86.8% on ALLEX vs. ~40% for baselines). No equations, fitted parameters, predictions, or first-principles derivations appear in the abstract or described content. Claims rest on direct experimental outcomes rather than any reduction to self-citations, ansatzes, or renamed inputs. The central attribution to architecture and system choices is presented as an empirical finding, not a mathematical necessity, making the work self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; the central claim rests on unstated assumptions about fair comparison and the causal role of the listed design choices.

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