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Reinforcement learning with verifiable rewards (RLVR) is often limited by insufficient exploration: difficult problems can yield uniformly incorrect rollout groups and therefore little learning signal. We show that such failures need not reflect missing capability. Instead, finite sampling often concentrates on a probl...
StrataVLA is introduced, a plug-and-play framework for hierarchical geometry grounding that achieves 98.53% average success on LIBERO suites while reducing geometry-model invocations by up to 88%, establishing hierarchical geometry injection as an effective and efficient way to achieve spatially grounded robotic contro...
AtVLA, a framework that inserts learnable register tokens into the visual encoder and improves the average LIBERO success rate, is introduced, a framework that inserts learnable register tokens into the visual encoder and improves the average LIBERO success rate.
Mechanistic analyses show that HAFI restores task-dependent spectral allocation while retaining semantic attention, establishing frequency enrichment as a distinct and effective route for improving VLM perception.
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