CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
CLAP converts pretrained VLMs to VLAs by prepending language descriptions to action tokens, avoiding distribution shift. Single-epoch fine-tuning yields 90.8% on LIBERO (+14.9 over VLA-0) and improved robustness. Open-weight models at 0.8B, 2B, 4B to be released.
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[Submitted on 9 Jul 2026]
Title:CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
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Abstract:Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control. Directly converting pretrained VLMs into VLAs with minimal architectural change offers a more transparent path to understanding how VLM capabilities transfer across model scales. The core obstacle is output-distribution mismatch: predicting actions as bare numeric token sequences moves generation away from the VLM's pretrained language distribution, degrading the capabilities we seek to preserve. To address this, we propose CLAP (Causal Language-Action Prediction), which prepends each numeric action sequence with a natural-language action description, causally conditioning precise action-token prediction on a language-action plan without modifying the backbone architecture. With single-epoch fine-tuning alone, 2B CLAP achieves 90.8% on LIBERO (+14.9 pt over VLA-0) and improves robustness on LIBERO-PRO under language, object, and spatial perturbations. We will release CLAP at 0.8B, 2B, and 4B as an open-weight, multi-scale compact VLA family from a single VLM lineage, enabling controlled analysis of VLM-to-VLA capability transfer.
Comments: Project website: this https URL
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.08974 [cs.RO]
(or arXiv:2607.08974v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.08974
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mai Nishimura [view email] [v1] Thu, 9 Jul 2026 22:34:11 UTC (2,186 KB)
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