Rethinking VLM Representation for VLA Initialization

• URL: http://arxiv.org/abs/2605.25802v1
• Date: Mon, 25 May 2026 12:51:35 GMT
• Title: Rethinking VLM Representation for VLA Initialization
• Abstract summary: Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones.<n>It remains unclear what kind of pretrained VLM representation is useful as a VLA initialization.
• Score: 44.662878535366985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is useful as a VLA initialization. In this paper, we study VLA initialization as a controlled representation-design problem along three axes: capability-level embodied VQA supervision, parameter-update strategy, and robot-data pretraining. Our experiments show that the original pretrained VLM representation is a key source of action performance. However, embodied VQA adaptation does not yield uniform gains: its benefit depends on downstream bottlenecks, and gains from different capability domains are not simply additive. For update strategy, LoRA provides a more reliable initialization than Full Finetune, indicating that overly reshaping the pretrained representation can weaken VLA initialization. Robot-data pretraining further improves VLA initialization, with the strongest variant obtained by staged LoRA-based training. Together, these findings suggest that effective VLM-to-VLA adaptation should inject action-relevant embodied and robot-trajectory signals while preserving the pretrained VLM representation that remains useful for action learning.

Related papers

• Two Bridges, One Pathway: From VLMs to Generalizable VLAs with Embodied Trajectory-Coupled Data [93.7685703383343]
  Vision-language models (VLMs) are powerful general-purpose reasoners, yet converting them into robot control policies is surprisingly difficult.<n>We argue that this gap can be bridged gradually with the right intermediate data.<n>We show that mixing task-relevant out-of-distribution ETC data with a small amount of action data enables the model to generalize to novel visual-language conditions.
  arXiv  Detail & Related papers  (2026-06-07T08:57:51Z)
• QuoVLA: Quotient Space for Vision-Language-Action Models [51.02329790939691]
  Vision-Language-Action (VLA) models commonly adapt pretrained VLM latents to robot control by mapping visual observations and language instructions to continuous actions.<n>Our textitQuotient Theory for VLA shows that pretrained VLM latents are not action-insufficient but action-sufficient.<n>We propose QuoVLA, a quotient-space framework for VLA that compresses pretrained VLM latents into action-sufficient representations.
  arXiv  Detail & Related papers  (2026-05-24T06:28:53Z)
• DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA [72.9197085473598]
  DIAL is a framework bridging high-level decision making and low-level motor execution through a differentiable latent intent bottleneck.<n>A VLM-based System-2 performs latent world modeling by latent visual foresight within the VLM's native feature space.<n>A lightweight System-1 policy then decodes this predicted intent together with the current observation into precise robot actions.
  arXiv  Detail & Related papers  (2026-03-31T15:02:27Z)
• On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning [61.38273866396522]
  Vision-Language-Action models have emerged as a powerful paradigm for general-purpose robot learning.<n>Current methods remain unsuitable for challenging simulated- or physical-world deployments.<n>We introduce a Test-Time Reinforcement Learning for VLAs framework that enables on-the-fly policy adaptation during inference.
  arXiv  Detail & Related papers  (2026-01-11T01:51:30Z)
• VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models [43.09726338623949]
  Vision-Language-Action (VLA) models integrate pretrained large Vision-Language Models (VLM) into their policy backbone.<n>This paper revisits a fundamental yet seldom systematically studied question: how VLM choice and competence translate to downstream VLA policies performance.<n>We introduce VLM4VLA, a minimal adaptation pipeline that converts general-purpose VLMs into VLA policies using only a small set of new learnable parameters.
  arXiv  Detail & Related papers  (2026-01-06T09:58:24Z)
• MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization [30.871663465403625]
  We present MAPS, the first robust fine-tuning framework for Vision-Language-Action (VLA) models.<n>Through systematic analysis, we uncover an empirical order in which proximity constraints should be relaxed to balance stability and flexibility.<n> MAPS linearly schedules this relaxation, enabling visual encoders to stay close to their pretrained priors while action-oriented language layers adapt more freely.
  arXiv  Detail & Related papers  (2025-11-25T03:39:37Z)
• Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization [42.41263928527529]
  Vision-Language-Action (VLA) models can endow agents with transferable world knowledge and vision-language grounding.<n>Yet when these VLMs are adapted to the action modality, it remains unclear to what extent their original visual representations and knowledge are preserved.<n>We conduct a systematic study of representation retention during VLA fine-tuning, showing that naive action fine-tuning leads to degradation of visual representations.
  arXiv  Detail & Related papers  (2025-10-29T15:20:10Z)
• VLA-R1: Enhancing Reasoning in Vision-Language-Action Models [35.264042764326895]
  Vision-Language-Action (VLA) models aim to unify perception, language understanding, and action generation.<n>Current VLA models often lack explicit step-by-step reasoning.<n>We present VLA-R1, a reasoning-enhanced VLA that integrates Reinforcement Learning from Verifiable Rewards.
  arXiv  Detail & Related papers  (2025-10-02T02:54:03Z)
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success [100.226572152954]
  We present an optimized fine-tuning recipe for vision-language-action models (VLAs)<n>Our recipe boosts OpenVLA's average success rate across four task suites from 76.5% to 97.1% while increasing action generation throughput by 26$times$.<n>In real-world evaluations, our fine-tuning recipe enables OpenVLA to successfully execute dexterous, high-frequency control tasks on a bimanual ALOHA robot.
  arXiv  Detail & Related papers  (2025-02-27T00:30:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.