Unverified50% confidenceFactExact time
LoRA fine-tuning is a roadmap item for the VKORE software layer, alongside inference acceleration and pretraining, and there is currently no public timeline.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
UnverifiedAccelerated LoRA is optimized during training for a specific sampler scheduling strategy and model weights; when the actual sampler's step size distribution, noise schedule, or model quantization method is inconsistent with the training conditions, the shortcut paths learned by the LoRA may fail entirely.69% similarVerifiedAfter LoRA training is complete, the low-rank matrices can be merged back into the original weights, introducing no additional latency during inference.69% similarVerifiedLoRA (Low-Rank Adaptation) was proposed by Microsoft in 2021, with the core idea of freezing the weights of the pretrained model and only training injected low-rank decomposition matrices.68% similarUnverifiedThe pretraining and fine-tuning paradigm originated from NLP and computer vision, with its core being large-scale data pretraining to learn general low-level features, and downstream tasks adapted using a small amount of labeled data.67% similarUnverifiedConstructing training data that includes counting, spatial relationships, and fine-grained attributes can mitigate the inherent deficiencies of VLMs at the pre-training stage.67% similar
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