Unverified50% confidenceOpinionExact time
Performance 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.
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9/12/2026
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UnverifiedConstructing training data that includes counting, spatial relationships, and fine-grained attributes can mitigate the inherent deficiencies of VLMs at the pre-training stage.75% 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73% similarUnverifiedWhen fine-tuning a visual large model for downstream tasks, one can choose to fine-tune only the backend LLM, only the frontend Encoder, both parts, or specify particular layers and modules to train.69% similarUnverified训练性能瓶颈来自 DataLoader，GPU 在等待数据供给过程中大量空转68% similarUnverifiedA hybrid approach is feasible: use NeMo during the training phase to optimize GPU utilization, then switch to a general-purpose inference engine such as vLLM during the deployment phase after exporting standard-format weights.67% similar
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