EvoVLA: Self-Evolving Vision-Language-Action Model

Vision-Language-Action (VLA) policies fine-tuned with RLand frozen VLM reward evaluators can produce observations that lookcorrect without physically completing the task—a phenomenon we for-malize as stage hallucination. To suppress it, we propose EvoVLA, aself-supervised framework with three synergistic modules: Stage-AlignedReward (SAR) penalizes near-miss states via counterfactual hard nega-tives in CLIP-based scoring; Pose-Based Object Exploration (POE)grounds curiosity in relative gripper–object pose; and Long-HorizonMemory uses selective context retrieval with gated fusion to stabi-lize credit assignment. We also introduce Discoverse-L, a long-horizonbenchmark (3 tasks, 18–74 stages) with a formal Hallucination Rate met-ric. Under matched backbones and budgets, EvoVLA achieves 69.2% suc-cess (+10.2 over OpenVLA-OFT), 1.5× sample efficiency, and reduceshallucination from 38.5% to 14.8%; real-robot deployment attains 54.6%(+11.0) across four tasks, confirming Sim2Real transfer.