EvoVLA: Self-Evolving Vision-Language-Action Model

• URL: http://arxiv.org/abs/2511.16166v1
• Date: Thu, 20 Nov 2025 09:08:33 GMT
• Title: EvoVLA: Self-Evolving Vision-Language-Action Model
• Authors: Zeting Liu, Zida Yang, Zeyu Zhang, Hao Tang,
• Abstract summary: Long-horizon robotic manipulation remains challenging for Vision-Language-Action (VLA) models.<n>We present EvoVLA, a self-supervised VLA framework that addresses this issue through three complementary components.<n>EvoVLA achieves one-and-a-half times better sample efficiency and reduces stage hallucination from 38.5 percent to 14.8 percent.
• Score: 11.746804244345613
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Long-horizon robotic manipulation remains challenging for Vision-Language-Action (VLA) models despite recent progress in zero-shot generalization and simulation-to-real-world transfer. Current VLA models suffer from stage hallucination, where agents exploit coarse evaluation signals to shortcut multi-step tasks, reporting high progress without truly completing them. We present EvoVLA, a self-supervised VLA framework that addresses this issue through three complementary components: Stage-Aligned Reward (SAR), which uses triplet contrastive learning with Gemini-generated hard negatives to prevent visual shortcuts; Pose-Based Object Exploration (POE), which grounds curiosity in relative object-gripper pose instead of raw pixels; and Long-Horizon Memory, which uses selective context retention and gated fusion to stabilize intrinsic shaping during extended rollouts. Extensive evaluations on Discoverse-L, a long-horizon manipulation benchmark with three multi-stage tasks, show that EvoVLA improves average task success by 10.2 percentage points over the strongest baseline (OpenVLA-OFT), reaching 69.2 percent. EvoVLA also achieves one-and-a-half times better sample efficiency and reduces stage hallucination from 38.5 percent to 14.8 percent. Real-world deployment on physical robots reaches an average success rate of 54.6 percent across four manipulation tasks, outperforming OpenVLA-OFT by 11 points, demonstrating effective sim-to-real transfer and strong generalization. Code: https://github.com/AIGeeksGroup/EvoVLA. Website: https://aigeeksgroup.github.io/EvoVLA.

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