Unverified50% confidenceOpinionExact time
VICO shifts VLM post-training from supervised manual annotation toward image-editing co-evolution, providing a scalable path beyond static-corpus RLVR.
1
Sources
50%
Confidence
Long-term
Relevance
10/9/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedPerformance 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.70% similarUnverifiedVLM training data is dominated by natural images and web documents, making it inherently ill-suited for highly dense, structured-layout documents like forms.69% 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68% similarUnverifiedVLA models unify visual perception, language understanding, and action generation within a single model framework, and their training is highly dependent on large-scale, high-quality human manipulation demonstration data.68% similarUnverifiedApplying LoRA or full-parameter fine-tuning to open-source image editing models to specifically train view transformation capability may be the most direct and effective route to replicating POV generation, but is highly dependent on the quality and quantity of training data containing image pairs of the same scene from different characters' perspectives.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/990706API
curl https://kongchang.com/api/v1/knowledge/claims/990706MCP
get_claim(id=990706)