Lost in Reconstruction: Aligning Action Representations with Language in Vision-Language-Action Models
arXiv:2608.10484v1 Announce Type: new Abstract: Action verbs describe not only the physical outcomes of actions, but also how those actions are performed. Yet action representations in vision-language-action models (VLAs) are typically optimized for reconstruction under L1/L2 losses in raw action space, where numerical proximity need not reflect linguistically meaningful distinctions. On BridgeV2, we show that action trajectories contain verb-grounding information beyond visual state changes, and that reconstruction-only discrete tokenization systematically erodes this information. To address this problem, we introduce SALT, a Semantically ALigned action Tokenizer that augments a VQ-VAE-style tokenizer with an auxiliary objective requiring a frozen vision-language model to recover the episode instruction from quantized action latents. Policies trained with SALT achieve 71.9% average success in SimplerEnv, compared with 42.7% for a reconstruction-only VQ-VAE tokenizer and 31.2% for FAST. SALT also develops verb-specialized codes while maintaining reconstruction fidelity. These results show that robot action trajectories provide a source of language grounding and that preserving this structure in action representations can substantially improve language-conditioned control.
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[Submitted on 11 Aug 2026]
Title:Lost in Reconstruction: Aligning Action Representations with Language in Vision-Language-Action Models
View a PDF of the paper titled Lost in Reconstruction: Aligning Action Representations with Language in Vision-Language-Action Models, by Li Wenjie and 5 other authors
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Abstract:Action verbs describe not only the physical outcomes of actions, but also how those actions are performed. Yet action representations in vision-language-action models (VLAs) are typically optimized for reconstruction under L1/L2 losses in raw action space, where numerical proximity need not reflect linguistically meaningful distinctions. On BridgeV2, we show that action trajectories contain verb-grounding information beyond visual state changes, and that reconstruction-only discrete tokenization systematically erodes this information. To address this problem, we introduce SALT, a Semantically ALigned action Tokenizer that augments a VQ-VAE-style tokenizer with an auxiliary objective requiring a frozen vision-language model to recover the episode instruction from quantized action latents. Policies trained with SALT achieve 71.9% average success in SimplerEnv, compared with 42.7% for a reconstruction-only VQ-VAE tokenizer and 31.2% for FAST. SALT also develops verb-specialized codes while maintaining reconstruction fidelity. These results show that robot action trajectories provide a source of language grounding and that preserving this structure in action representations can substantially improve language-conditioned control.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2608.10484 [cs.RO]
(or arXiv:2608.10484v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.10484
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Li Wenjie [view email] [v1] Tue, 11 Aug 2026 04:57:17 UTC (5,504 KB)
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