Unverified50% confidenceOpinionExact time
The parameters of pretrained VLAs are already highly specialized for action generation; if the same backbone is made to simultaneously handle future prediction and action generation, the gradient directions may conflict with each other, causing the representation to be pulled apart.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedStandard VLM policies are usually pretrained only on image-text data, and their action capability relies on subsequent fine-tuning or an additional policy head, so their action priors are relatively thin.79% similarUnverifiedPretrained vision-language-action models (VLA) tend to lose coherence midway and fail when faced with unseen long-horizon tasks.75% similarUnverifiedCompared 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.74% 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70% similarUnverifiedVioLA changes the prediction target of the generalist policy by predicting body and hand motion latents rather than directly outputting joint commands, and the motion latents are executed on the robot by pretrained body and hand controllers.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1038909API
curl https://kongchang.com/api/v1/knowledge/claims/1038909MCP
get_claim(id=1038909)