DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions

• URL: http://arxiv.org/abs/2605.31271v1
• Date: Fri, 29 May 2026 13:03:16 GMT
• Title: DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions
• Abstract summary: We propose DriveMA, a Driving VLA framework built on verifiable meta-actions.<n>DriveMA exploits this verifiability with action-centric supervised training and a data-efficient turn-level credit assignment reinforcement learning framework.<n>We show that even a simple meta-action interface can achieve state-of-the-art planning when made verifiable and optimized for language-action alignment.
• Score: 31.04597401352966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise. We propose DriveMA, a Driving VLA framework built on verifiable meta-actions, which summarize future ego motion into compact language-domain intentions and can be constructed from expert trajectories with a trajectory-grounded annotation pipeline and can be verified against generated trajectories through rule-based projection. DriveMA exploits this verifiability with action-centric supervised training and a data-efficient turn-level credit assignment reinforcement learning framework, explicitly aligning high-level decisions with low-level trajectory planning through dense rewards and precise credit assignment. DriveMA sets a new state of the art on the Waymo Open Dataset Vision-based E2E Driving, achieving a Rater Feedback Score of 8.060 with a 2B model and further improving it to 8.079 with a 4B model; it also obtains competitive closed-loop planning performance on NAVSIM. These results show that even a simple meta-action interface can achieve state-of-the-art planning when made verifiable and optimized for language-action alignment. Code, data, and models will be released to facilitate future research.

Related papers

• LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving [13.964957969409655]
  We develop the Layer-Wise World-Model-Guided Driving framework (LWDrive)<n>LWDrive is a VLM planning framework that refines coarse trajectories through layer-wise world-model guidance.<n> Experiments show that LWDrive achieves a score of 92.0 on the NAVSIM benchmark and 89.6 on NAVSIM-v2.
  arXiv  Detail & Related papers  (2026-06-29T07:17:13Z)
• DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions [31.04597401352966]
  In this paper, we show that concise one-step meta-actions are a simple yet effective alternative to verbose reasoning.<n>We propose DriveMA, which combines action-centric supervised training with a turn-level credit-assignment reinforcement learning framework.<n>DriveMA achieves a new state of the art on the End-to-End Driving Challenge with a 2B model, reaching a Rater Feedback Score (RFS) of 8.060, while its 4B version further improves the state of the art to 8.079.
  arXiv  Detail & Related papers  (2026-05-20T15:05:36Z)
• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• EvoDriveVLA: Evolving Autonomous Driving Vision-Language-Action Model via Collaborative Perception-Planning Distillation [58.84721000276226]
  EvoDriveVLA is a novel collaborative perception-planning framework.<n>It integrates self-anchored perceptual constraints and oracle-guided trajectory optimization.<n>EvoDriveVLA achieves SOTA performance in open-loop evaluation and significantly enhances performance in closed-loop evaluation.
  arXiv  Detail & Related papers  (2026-03-10T10:19:07Z)
• Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future [125.92052530850425]
  Vision-Language-Action (VLA) frameworks integrate perception with language-grounded decision making.<n>VLA frameworks offer a pathway toward more interpretable, generalizable, and human-aligned driving policies.<n>This work aims to establish a coherent foundation for advancing human-compatible autonomous driving systems.
  arXiv  Detail & Related papers  (2025-12-18T16:57:44Z)
• AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning [37.176428069948535]
  Vision-Language-Action (VLA) models have shown promise for end-to-end autonomous driving.<n>Current VLA models struggle with physically infeasible action outputs, complex model structures, or unnecessarily long reasoning.<n>We propose AutoVLA, a novel VLA model that unifies reasoning and action generation within a single autoregressive generation model.
  arXiv  Detail & Related papers  (2025-06-16T17:58:50Z)
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving [49.07731497951963]
  ReCogDrive is a novel Reinforced Cognitive framework for end-to-end autonomous driving.<n>We introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers.<n>We then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner.
  arXiv  Detail & Related papers  (2025-06-09T03:14:04Z)
• End-to-End Driving with Online Trajectory Evaluation via BEV World Model [52.10633338584164]
  We propose an end-to-end driving framework WoTE, which leverages a BEV World model to predict future BEV states for Trajectory Evaluation.<n>We validate our framework on the NAVSIM benchmark and the closed-loop Bench2Drive benchmark based on the CARLA simulator, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-04-02T17:47:23Z)
• VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision [17.36342349850825]
  Vision-language models (VLMs) as teachers to enhance training by providing additional supervision.<n>VLM-AD achieves significant improvements in planning accuracy and reduced collision rates on the nuScenes dataset.
  arXiv  Detail & Related papers  (2024-12-19T01:53:36Z)
• Enhancing End-to-End Autonomous Driving with Latent World Model [78.22157677787239]
  We propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving.<n> LAW predicts future scene features based on current features and ego trajectories.<n>This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks.
  arXiv  Detail & Related papers  (2024-06-12T17:59:21Z)

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.