The Tool Desk
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What the two models actually are
REKI and OpenVLA are both vision-language-action (VLA) policies: they take visual observations and language or task context, then produce robot actions. They are not interchangeable drop-in controllers. Their model size, temporal handling, training mixture, released checkpoints and license terms affect how much engineering work is needed before a robot can move safely.
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REKI in one view
Kyutai describes REKI as a 2.4-billion-parameter VLA. Its policy is split into a vision-language model (VLM) and a flow-matching action expert. Register tokens in the action expert remain in the inference cache and summarize recent VLM outputs. This gives REKI an explicit short-term memory mechanism without requiring the full visual-language stack to be recomputed as expensively at every decision.
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Kyutai says REKI was pretrained only on public data and releases code, data-processing, training and evaluation pipelines, deployment support, and pretrained or fine-tuned weights. The published model table lists checkpoints for Franka Panda in LIBERO, ALOHA in RoboTwin, PandaOmron in RoboCasa365, and Fourier GR-1 in RoboCasa-GR1.
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OpenVLA in one view
The OpenVLA repository describes openvla-7b as a seven-billion-parameter model built from the Prismatic prism-dinosiglip-224px VLM. It combines DINOv2 and SigLIP vision features with a Llama-2 language model. Its training mixture spans 970,000 trajectories from the Open X-Embodiment collection. The repository also provides an established open implementation and a fine-tuning workflow that many teams use as their starting point.
These descriptions point to different strengths rather than a simple “new model beats old model” story. REKI concentrates on a smaller architecture with an explicit cache-based memory design and named embodiment checkpoints. OpenVLA offers a larger baseline trained across a broad collection of robot embodiments and a mature, recognizable software stack.
REKI vs OpenVLA: the published facts
| Decision factor | Kyutai REKI | OpenVLA |
|---|---|---|
| Published model size | 2.4 billion parameters (Kyutai, 2026) | 7 billion parameters for openvla-7b (OpenVLA repository) |
| Core design | Vision-language model followed by a flow-matching action expert; register-token cache summarizes recent VLM outputs | Prismatic VLM using DINOv2 and SigLIP vision features with a Llama-2 language model |
| Published training scale | 200,000 pretraining steps on 32 H100 GPUs (Kyutai, 2026) | 970,000 trajectories from the Open X-Embodiment mixture |
| Published latency figure | 21.8 ms for one compiled action chunk from two 256×256 images on one H100 with ten Euler steps | No directly comparable latency figure is established in the cited OpenVLA materials |
| Named robot checkpoints | Franka Panda/LIBERO; ALOHA/RoboTwin; PandaOmron/RoboCasa365; Fourier GR-1/RoboCasa-GR1 | Broad Open X-Embodiment pretraining and an open fine-tuning stack; the cited material does not present the same checkpoint table |
| Data position | Kyutai emphasizes pretraining exclusively on public data and releases training and evaluation tooling | OpenVLA emphasizes its 970,000-trajectory Open X-Embodiment mixture |
| License warning | REKI’s LIBERO model card lists CC BY-NC-SA 4.0 for weights and MIT for code | Code is MIT, but the repository warns that pretrained weights may inherit restrictions from upstream base models, including Llama licensing |
The table is a record of what the projects publish, not a benchmark ranking. Parameter count is not a guaranteed throughput number, and a latency number measured on an H100 cannot be transferred directly to an embedded computer or a different inference implementation.
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REKI’s 2.4B parameters are roughly one-third of OpenVLA’s 7B parameter count. A smaller model generally reduces weight-memory pressure and can make it easier to reserve GPU capacity for image preprocessing, robot middleware and safety monitors. It can also reduce the cost of moving weights through memory during inference. Those are engineering advantages, not a promise that every REKI deployment will run faster than every OpenVLA deployment.
Actual speed depends on precision, compiler, batching, image transforms, action-chunk length, number of flow or decoding steps, camera count, and the processor in the target robot. A carefully optimized 7B deployment can outperform an unoptimized 2.4B deployment, while a small model can be the practical choice when a robot computer cannot hold the larger one with enough headroom.
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When the 2.4B footprint is decisive
- The policy must share a GPU with perception, planning or a simulator.
- You need predictable memory use rather than maximum representation capacity.
- Your robot has a limited on-board computer and cannot stream every observation to a larger server.
- You want to run several policy replicas for testing, fallback or multi-camera experiments.
When a 7B baseline is acceptable
- Your existing serving hardware already accommodates OpenVLA and its surrounding software.
- You value continuity with experiments built on the Open X-Embodiment baseline.
- You plan to fine-tune and have the storage and memory needed for the larger checkpoint.
Latency: what REKI’s 21.8 ms does and does not tell you
Kyutai reports 21.8 milliseconds for one compiled action chunk generated from two 256×256 images on one H100, using ten Euler steps. Under that stated setup, the neural-policy portion is fast enough to be relevant to high-rate control loops. The conditions are essential: the result uses an H100, compiled execution, two fixed-resolution images and a specified number of Euler steps. It is not a claim that a complete robot cycle takes 21.8 ms.
A real cycle also includes camera capture and synchronization, image resizing, transfers between CPU and accelerator memory, policy scheduling, action post-processing, communication with the robot controller and any safety checks. If the policy emits a chunk rather than one low-level command, the control frequency is determined by both inference cadence and how the robot executes that chunk.
The cited OpenVLA materials do not provide a matching result measured with the same images, action chunk, hardware, precision and preprocessing. Therefore, it is not valid to subtract REKI’s number from an assumed OpenVLA number or declare a universal latency winner. Measure both policies in the exact pipeline you intend to deploy.
A useful latency test
- Freeze the camera resolution, crop, color conversion and frame rate for both policies.
- Use the same accelerator, numerical precision and software-compilation strategy wherever each model supports them.
- Measure image-to-action latency separately from camera and robot-network latency.
- Record median, 95th-percentile and worst observed times, not only a single fastest run.
- Test sustained operation for the length of a task; memory growth, thermal throttling and queue buildup can matter more than a short benchmark.
- Report action-chunk size and the number of Euler or decoding steps with every result.
Temporal context and short-term memory
REKI explicitly keeps register tokens in the action expert’s cache. Those tokens summarize recent VLM outputs, giving the policy a compact form of short-term temporal context at low inference cost. This is useful when the current image alone is ambiguous: for example, an object may be briefly occluded, a gripper may be approaching from the side, or the robot may need continuity across a small camera-motion change.
The OpenVLA materials cited here describe its architecture and training mixture but do not document the same register-token memory mechanism. That does not prove OpenVLA cannot use temporal information in a particular deployment; it means you should not assume an equivalent built-in cache from the published description. If your task depends on recent-frame context, test whether your OpenVLA pipeline supplies it through frame stacking, an external state estimator, recurrent changes or fine-tuning.
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Questions to ask in a temporal-context trial
- Does performance drop when the target is hidden for one or two frames?
- Can the policy resume a partially completed grasp after a brief camera interruption?
- Does adding extra frames increase latency enough to cancel the accuracy benefit?
- Are cached features reset at episode boundaries so information from one task cannot leak into the next?
Robot and embodiment fit
If your robot matches one of REKI’s named checkpoints, that is a concrete starting advantage. The official release table identifies Franka Panda with LIBERO data, ALOHA with RoboTwin, PandaOmron with RoboCasa365, and Fourier GR-1 with RoboCasa-GR1. A checkpoint does not remove the need to verify camera placement, joint limits, action representation and controller conventions, but it can reduce the distance between published evaluation and your first experiment.
OpenVLA’s distinguishing evidence is breadth: its 970,000-trajectory Open X-Embodiment mixture spans many embodiments, and its open repository supports fine-tuning. That breadth can be valuable when your robot is not represented by a REKI release or when your team already has data and scripts organized around OpenVLA. Broad pretraining is not a guarantee that an unseen robot will work without adaptation; embodiment-specific calibration and data remain necessary.
Franka Panda
For a Franka project, REKI has an explicitly named LIBERO checkpoint. Start by confirming that your camera viewpoint, action space, gripper convention and task distribution match the checkpoint’s assumptions. OpenVLA remains attractive if your team has an existing Open X-Embodiment or OpenVLA fine-tuning pipeline and can collect demonstrations in the same format.
ALOHA
REKI lists an ALOHA checkpoint associated with RoboTwin. ALOHA setups can differ substantially in camera synchronization and dual-arm action conventions, so verify those interfaces before treating the checkpoint as plug-and-play. OpenVLA’s broader pretraining may help as a baseline, but the cited material does not establish a direct ALOHA comparison.
Other robots
For PandaOmron and Fourier GR-1, REKI’s named RoboCasa checkpoints provide an obvious experiment path. For a robot outside that list, compare the amount of adaptation work rather than choosing by parameter count alone: examine available demonstrations, action dimensionality, camera geometry, calibration tools and the fine-tuning code your team can maintain.
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Data, reproducibility and fine-tuning choices
Kyutai says REKI’s pretraining uses public data exclusively and that it releases data-processing, training and evaluation pipelines in addition to weights. This emphasis is useful when you need to inspect how examples are prepared or reproduce an evaluation rather than treating a checkpoint as an opaque artifact. The published training figure is 200,000 pretraining steps on 32 H100 GPUs, which describes the scale of that run, not the compute needed for your own adaptation.
OpenVLA’s 970,000-trajectory Open X-Embodiment mixture is a substantial starting point for a generalist policy. Its repository’s established fine-tuning workflow can lower integration cost for teams that already use its data formats and tooling. You still need to check that the data license, robot interfaces and preprocessing choices fit your project.
Choose REKI’s workflow when
- You want to inspect or modify the complete data-to-deployment path released by Kyutai.
- Your target embodiment has one of the published fine-tuned checkpoints.
- You want explicit temporal-cache behavior and a smaller base policy.
Choose OpenVLA’s workflow when
- Your lab already has OpenVLA-compatible demonstrations, evaluation scripts or checkpoints.
- You need the seven-billion-parameter Open X-Embodiment reference point for an internal study.
- Your adaptation plan depends on the repository’s existing fine-tuning path more than on a named REKI checkpoint.
Commercial use and licensing: check the exact artifact
There is no single “commercially safe” answer for either project without identifying the exact code, weights and upstream components you will ship. REKI’s LIBERO model card lists CC BY-NC-SA 4.0 for the weights and MIT for the code. The non-commercial clause in that weights license is material for a product or paid service; obtain legal advice before using those weights commercially, and do not assume the MIT code license changes the weights’ terms.
OpenVLA’s repository states that its code is released under MIT, while pretrained models may inherit restrictions from the underlying base models. The repository specifically warns that upstream components can impose additional conditions, including Llama licensing. Read the license attached to the exact checkpoint and every upstream component, then document attribution, redistribution rights, modification rules and any field-of-use restrictions.
A practical compliance checklist
- Record the exact checkpoint name, revision and download source.
- Save the weight license and the code license separately; they may differ.
- List upstream vision and language components and read their terms.
- Ask counsel whether your use is internal research, a paid service, an embedded product or redistribution; those categories can have different obligations.
- Keep notices and attribution files in the software and model package as required.
- Recheck terms when replacing a checkpoint or adding a fine-tuned derivative.
How to run a fair REKI-versus-OpenVLA evaluation
A useful comparison controls the variables that most often make model benchmarks misleading. The goal is not to reproduce a headline number; it is to find which policy meets your robot’s task and safety requirements with acceptable engineering effort.
- Define the target contract. Write down the robot embodiment, camera count and placement, image resolution, action representation, control frequency, maximum acceptable latency and safety stop behavior.
- Select compatible checkpoints. Use a REKI checkpoint that matches your embodiment where available. For OpenVLA, record the exact
openvla-7brevision or fine-tuned model and its preprocessing assumptions. - Normalize inputs. Apply each project’s required image transforms, but keep camera hardware, exposure, cropping policy and synchronization identical.
- Normalize action handling. Convert outputs into the same robot command interface only after documenting each model’s native action units, chunk length and gripper convention.
- Measure system latency. Capture timestamps at camera arrival, preprocessing completion, model output, safety filtering and robot-command transmission.
- Use task splits. Separate familiar tasks, held-out object arrangements and perturbation cases such as occlusion or a moved camera. Do not tune on the held-out set.
- Track safety outcomes. Count collisions, workspace violations, emergency stops, dropped objects and unsafe commands, not only task success.
- Report adaptation cost. Include demonstration collection, fine-tuning time, memory usage, debugging effort and the amount of robot-specific code.
Common failure modes and how to diagnose them
The policy is fast in isolation but slow on the robot
Profile camera transfer, resizing, serialization, middleware queues and safety checks. REKI’s 21.8 ms figure covers the stated compiled action-chunk setup, not every part of a robot control loop. If queues grow, reduce avoidable copies, shorten the pipeline or lower the requested control rate only after confirming that the action chunk remains safe.
Actions look plausible but the arm moves incorrectly
Check coordinate frames, joint ordering, units, normalization ranges, gripper open/close encoding and the action horizon. A checkpoint trained for one convention can produce apparently reasonable numbers that are wrong for another controller.
Performance collapses after a camera change
Recheck viewpoint, lens distortion, image scaling, exposure and temporal synchronization. A model can be sensitive to visual distribution even when the robot and task are nominally the same. Collect a small, representative validation set before deciding that the architecture is unsuitable.
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For REKI, verify cache reset and update behavior, because its register-token memory is part of the action-expert inference path. For OpenVLA, determine whether your deployment supplies multiple frames or another temporal mechanism; the cited architecture description does not establish an equivalent built-in cache.
A commercial review blocks release
Separate code from weights in your bill of materials and audit upstream components. REKI’s cited LIBERO weights terms and OpenVLA’s upstream-license warning are reasons to perform this check before collecting product commitments, not after deployment.
A decision framework for your robot project
| If your top constraint is… | Start with… | Reason |
|---|---|---|
| On-robot memory or serving budget | REKI | Its published 2.4B size is substantially smaller than OpenVLA’s 7B model. |
| Explicit recent-frame context | REKI | Register tokens and cache reuse are part of the documented architecture. |
| A named Franka, ALOHA, PandaOmron or Fourier GR-1 checkpoint | REKI | Those embodiment-specific releases are listed by Kyutai. |
| An established Open X-Embodiment baseline | OpenVLA | Its training mixture contains 970,000 trajectories and its repository provides a known fine-tuning path. |
| Existing OpenVLA infrastructure | OpenVLA | Reuse of data formats, scripts and team experience can outweigh theoretical architecture differences. |
| Commercial shipment | Neither without legal review | REKI’s cited weights license is non-commercial, while OpenVLA warns of upstream restrictions. |
Bottom line: which one fits?
REKI is the stronger default for a constrained on-robot deployment, a project that benefits from explicit short-term visual memory, or a robot covered by one of Kyutai’s released fine-tuned checkpoints. Its 2.4B size and published 21.8 ms H100 result make it an appealing candidate for a low-latency experiment, provided you preserve the benchmark conditions when interpreting that number.
OpenVLA is the better starting point when your team already works in its repository, needs a seven-billion-parameter Open X-Embodiment reference model, or intends to build on its established fine-tuning workflow. Its broader training mixture is a useful foundation, but it does not remove the need for embodiment-specific validation.
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For either choice, the decisive evidence is your controlled trial on the target robot: identical cameras and action interfaces, measured end-to-end latency, held-out tasks, disturbance tests and a documented license review. The published sources do not establish a universal winner on matched hardware.
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