Verified75% confidenceTradeoffExact time
Compared with traditional end-to-end imitation learning, VLA models have stronger generalization capabilities, but high inference latency, stringent requirements for training data diversity, and deployment on real-time control and low-cost hardware remain the primary challenges.
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10/11/2026
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UnverifiedEffectively combining hardware-aware training and adaptation mechanisms with large-scale VLA pretraining remains an open problem.80% similarUnverifiedPretrained vision-language-action models (VLA) tend to lose coherence midway and fail when faced with unseen long-horizon tasks.79% similarUnverified微调VLA模型时通常采用分层学习率策略：视觉编码器使用极小学习率或冻结，语言模型层使用中等学习率，动作专家使用较大学习率79% similarUnverifiedPerformance bottlenecks in VLMs, beyond the visual encoder, may also stem from the connector/projector module, the language model's instruction-following capability, and training data.76% similarUnverifiedVKORE's route involves trade-offs: reinventing a cross-hardware portable training instruction set will lead to a substantial rise in software complexity and may lose part of the hardware-level performance optimization space.75% similar
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