REKI: Registering Knowledge in VLAs
Abstract
We introduce REKI, a 2.4B vision-language-action model (VLA) pretrained exclusively on public data that sets a new Pareto frontier between performance and model size: on LIBERO-Plus, RoboTwin Clean2Rand and RoboCasa365, it is outperformed only by models about twice its size. REKI consists of a vision-language model (VLM) followed by a flow-matching action expert, and is pretrained on a curated mixture of public robot, egocentric human and vision-language data. Additionally, we endow it with a memory that gives the policy access to its recent past at negligible cost: a few register tokens in the action expert summarise the output of the VLM at each frame and persist in the expert's cache. This memory improves success in simulation and on real robots, and makes oscillations between poses about four times rarer. REKI uses a simple model design and training paradigm, and is optimised to be efficient at training and inference. We release the full code across data curation, training, inference and evaluation, deployment, and all pretrained and finetuned model weights: https://github.com/kyutai-labs/reki.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
REKI introduces an efficient 2.4B VLA model with a novel register-token memory mechanism that achieves state-of-the-art performance on key robotics benchmarks while maintaining a small model size. The combination of flow-matching action experts, public-data pretraining, and a lightweight temporal memory strategy provides a robust and reproducible baseline that advances the field of vision-language-action modeling by balancing performance with computational efficiency.
Methodology
The paper proposes REKI, a 2.4B parameter Vision-Language-Action (VLA) model. The architecture consists of a standard Vision-Language Model (VLM) backbone followed by a flow-matching action expert. The primary methodological contribution is the introduction of a lightweight memory mechanism using "register tokens" within the action expert. These tokens summarize the VLM's output at each frame and persist in the expert's cache, allowing the policy to access recent history with negligible computational overhead. The model is pretrained on a curated mixture of public robot, egocentric human, and vision-language data. The approach is notable for its efficiency and the specific design choice of using register tokens for temporal context rather than heavier recurrent or transformer-based memory blocks.
Results & evaluation
The evaluation is conducted on standard robotics benchmarks: LIBERO-Plus, RoboTwin Clean2Rand, and RoboCasa365. The paper claims REKI sets a new Pareto frontier, outperforming models of similar size and being outperformed only by models approximately twice its size. Specific empirical findings include a four-fold reduction in pose oscillations due to the memory mechanism. The inclusion of real-robot evaluation strengthens the claims, though the full text provided is largely a structural outline, so specific numerical comparisons must be inferred from the abstract's strong claims.
Reproducibility
The paper explicitly states that full code for data curation, training, inference, evaluation, deployment, and all pretrained/finetuned weights are released. This high level of open-sourcing significantly enhances reproducibility and community utility.
Limitations
The provided text is a preprint skeleton with section headers but lacks the detailed body text for rigorous scrutiny of specific ablation studies or failure cases. The reliance on "public data" may limit performance compared to models trained on proprietary, high-quality datasets, though the paper argues for efficiency. The "register token" memory is described as having "negligible cost," but the exact latency trade-offs in high-frequency control loops need verification in the full text.
Broader impact
By providing a strong, efficient baseline that is fully open-sourced, REKI lowers the barrier to entry for VLA research. The memory mechanism could be adopted by other architectures to improve temporal consistency in robotic control. The focus on public data pretraining is significant for democratizing access to capable robot policies. REKI introduces an efficient 2.4B VLA model with a novel register-token memory mechanism that achieves state-of-the-art performance on key robotics benchmarks while maintaining a small model size. The combination of flow-matching action experts, public-data pretraining, and a lightweight temporal memory strategy provides a robust and reproducible baseline that advances the field of vision-language-action modeling by balancing performance with computational efficiency.
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