ArXiv

LA4VLA: Learning to Act without Seeing via Language-Action Pretraining

2026-06-25 · 17:13 UTC ·Tao Lin, Yuxin Du, Yiran Mao... ·1 min read
Authors
Tao Lin, Yuxin Du, Yiran Mao...
Categories
cs.RO
arXiv
https://arxiv.org/abs/2606.27295v1
PDF
https://arxiv.org/pdf/2606.27295v1

Brief

LA4VLA proposes language-action pretraining to teach language-conditioned action priors without visual inputs, addressing VLA policies' tendency to rely on visual shortcuts when visual-action signals dominate sparse language-action supervision. The method decomposes demonstrations into atomic action segments with low-level action descriptions (LA4-33K) and trains a 1B-parameter model (LA4VLA-1B). Across simulation and real robot tests, LA-pretraining outperforms matched VLA pretraining and mixed LA+VLA yields the largest gains (up to +17.8 pp sim, +45.0 pp real). Summary based on the paper abstract; full text was not reviewed.

Why it matters

LA4VLA constructs LA4-33K, a dataset of 33,000 Language-Action (LA) episodes by decomposing expert demonstration trajectories into atomic action segments paired with low-level action descriptions, created without additional robot data collection.

Key details

  • The authors train a lightweight LA4VLA-1B (1 billion parameters) VLA model and compare three pretraining paradigms (LA-only, sequential LA→VLA, mixed LA+VLA); mixed LA-VLA pretraining raises average success rates versus no-pretraining by up to 17.8 percentage points in simulation and 45.0 percentage points in real-world tasks.
Source evidence

Abstract

Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to actions. However, dense visual-action supervision can dominate the comparatively sparse language-action signal. As a result, policies may rely on visual shortcuts rather than learn how language conditions action execution, making them sensitive to visual variations. To address this limitation, we propose LA4VLA, a language-action pretraining framework that enables policies to acquire language-conditioned action priors without visual observations. These priors capture reusable manipulation skills shared across tasks and scenes, reducing reliance on scene-specific visual cues. Specifically, LA4VLA decomposes expert demonstration trajectories into atomic action segments and pairs each segment with a corresponding low-level action description. This yields LA4-33K, a dataset of 33K Language-Action (LA) episodes derived entirely from existing demonstrations without additional robot data collection. We further develop LA4VLA-1B, a lightweight 1B-parameter VLA model, and investigate three paradigms for incorporating language-action supervision into VLA learning: LA-only pretraining, sequential LA-to-VLA pretraining, and mixed LA-VLA pretraining. Across simulation and real-world tasks, LA-pretrained policies consistently outperform matched VLA-pretrained counterparts, while combining LA and VLA supervision leads to further gains. In particular, mixed LA-VLA pretraining improves the average success rate of LA4VLA-1B over the no-pretraining baseline by up to 17.8 and 45.0 percentage points in simulation and real-world tasks, respectively. These results establish LA4VLA as an effective and complementary pretraining strategy for building stronger and more robust VLA policies.

Comment: Github: https://github.com/MINT-SJTU/LA4VLA