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New REKI VLA model balances performance and size with memory feature

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced REKI, a 2.4 billion parameter vision-language-action model (VLA) that achieves a new balance between performance and size. Pretrained on public data, REKI incorporates a memory mechanism using register tokens to access recent past information, which reportedly reduces oscillations and improves success rates on simulations and real robots. The model's design prioritizes efficiency in both training and inference, and its creators have released the full code and pretrained weights. AI

IMPACT This model's efficient design and memory feature could influence future development of robotics and embodied AI systems.

RANK_REASON The cluster describes a new research paper detailing a novel model architecture and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New REKI VLA model balances performance and size with memory feature

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The cluster describes a new research paper detailing a novel model architecture and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 Deutsch(DE) · Juliette Marrie, Paul Pacaud, Adrien Ramanana Rahary, Shizhe Chen, Moritz B\"ohle ·

    REKI: Registering Knowledge in VLAs

    arXiv:2610.22178v1 Announce Type: cross 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 outp…

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