Pith Number

pith:WUMYKSR7

pith:2025:WUMYKSR74ZAYOLM2FTE775KAD2

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DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving

Bing Zhan, Chufeng Tang, Haochen Wang, Lue Fan, Lu Hou, Shuyao Shang, Weisong Liu, Xiaoman Wang, Yasong An, Yingyan Li, Yuntao Chen, Yuqi Wang, Zhaoxiang Zhang

Adding world modeling to predict future images lets vision-language-action models use large driving datasets more effectively and accelerate performance gains as data scales.

arxiv:2510.12796 v2 · 2025-10-14 · cs.CV · cs.AI

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Record completeness

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

we propose DriveVLA-W0, a training paradigm that employs world modeling to predict future images. ... Crucially, it amplifies the data scaling law, showing that performance gains accelerate as the training dataset size increases.

C2weakest assumption

That the added world modeling task of predicting future images supplies a dense, unbiased self-supervised signal that meaningfully utilizes unused model capacity without requiring extra labels or introducing new failure modes in driving dynamics.

C3one line summary

DriveVLA-W0 adds world modeling to predict future images in VLA models, overcoming sparse action supervision and amplifying data scaling laws on NAVSIM benchmarks and a large in-house dataset.

References

39 extracted · 39 resolved · 15 Pith anchors

[1] Covla: Comprehensive vision-language-action dataset for autonomous driving 2025

[2] Qwen2.5-VL Technical Report · arXiv:2502.13923

[3] Scaling Laws of Mo- tion Forecasting and Planning – Technical Report

[4] Vavim and vavam: Autonomous driving through video generative modeling

[6] $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control · arXiv:2410.24164

Formal links

2 machine-checked theorem links

Cited by

27 papers in Pith

ChainFlow-VLA: Causal Flow Planning with Vision-Language Models

LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model

Grounding Driving VLA via Inverse Kinematics

VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving

CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning

Receipt and verification

First computed	2026-05-17T23:38:14.789505Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b519854a3fe641872d9a2cc9fff5401e9cd4a492cba5a529daca1605f6176e25

Aliases

arxiv: 2510.12796 · arxiv_version: 2510.12796v2 · doi: 10.48550/arxiv.2510.12796 · pith_short_12: WUMYKSR74ZAY · pith_short_16: WUMYKSR74ZAYOLM2 · pith_short_8: WUMYKSR7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/WUMYKSR74ZAYOLM2FTE775KAD2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b519854a3fe641872d9a2cc9fff5401e9cd4a492cba5a529daca1605f6176e25

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-10-14T17:59:47Z",
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