Pith Number

pith:ILI2JTPC

pith:2025:ILI2JTPC2LAVCZ6V2MSBGJ7RUV

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VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning

Chubin Zhang, Guanxing Lu, Haonan Jiang, Wenkai Guo, Yansong Tang, Yuheng Zhou, Zifeng Gao, Ziwei Wang

VLA-RL applies online reinforcement learning to raise pretrained vision-language-action models above finetuned baselines on robot tasks.

arxiv:2505.18719 v1 · 2025-05-24 · cs.RO · cs.AI

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\pithnumber{ILI2JTPC2LAVCZ6V2MSBGJ7RUV}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

VLA-RL enables OpenVLA-7B to surpass the strongest finetuned baseline by 4.5% on 40 challenging robotic manipulation tasks in LIBERO, and even matches the performance of advanced commercial models such as π₀-FAST.

C2weakest assumption

That a vision-language model fine-tuned on pseudo reward labels from automatically extracted task segments will provide sufficiently accurate and generalizable rewards for online RL across diverse out-of-distribution scenarios.

C3one line summary

VLA-RL applies online RL to pretrained VLAs, yielding a 4.5% gain over strong baselines on 40 LIBERO manipulation tasks and matching commercial models like π₀-FAST.

References

93 extracted · 93 resolved · 36 Pith anchors

[1] Proceedings of Advances in Neural Information Processing Systems (NeurIPS) 35, 28955–28971 (2022) 2, 3 2022

[2] Solving Rubik's Cube with a Robot Hand 1910 · arXiv:1910.07113

[3] Univg-r1: Reasoning guided universal visual grounding with reinforcement learning 2025

[4] Proceedings of Advances in Neural Information Processing Systems (NeurIPS) 35, 24639– 24654 (2022) 2 2022

[5] In: Proceedings of International Conference on Machine Learning (ICML) 2023

Cited by

35 papers in Pith

Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models

Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning

Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning

TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation

VLANeXt: Recipes for Building Strong VLA Models

Receipt and verification

First computed	2026-05-17T23:38:47.803799Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

42d1a4cde2d2c15167d5d3241327f1a56bc45dbacb36a1d56519d8c5a32a62f1

Aliases

arxiv: 2505.18719 · arxiv_version: 2505.18719v1 · doi: 10.48550/arxiv.2505.18719 · pith_short_12: ILI2JTPC2LAV · pith_short_16: ILI2JTPC2LAVCZ6V · pith_short_8: ILI2JTPC

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ILI2JTPC2LAVCZ6V2MSBGJ7RUV \
  | 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: 42d1a4cde2d2c15167d5d3241327f1a56bc45dbacb36a1d56519d8c5a32a62f1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "de19876faf1eddbde8804dc887eb945ac2b1a2b9861aa7505585119aa0f575e2",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2025-05-24T14:42:51Z",
    "title_canon_sha256": "b33a2cb43154b37dea226bf00ad80291d2967096372f3629d2434c613d22b641"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2505.18719",
    "kind": "arxiv",
    "version": 1
  }
}