Author

Zihan Tang

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Preprint Aug 2026

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation

SLIM (Self-supervised Latent Interaction Model), a compact 0.5B-parameter latent interaction policy, which matches or exceeds representative large-scale VLA and world-action-model baselines with fewer parameters, no additional embodied pretraining, lower inference latency, and substantially lower GPU memory usage.

Jingkai Wang, Zihan Tang, Gu Zhang et al. · 0 citations