Unverified50% confidenceOpinionExact time
Effectively combining hardware-aware training and adaptation mechanisms with large-scale VLA pretraining remains an open problem.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCompared 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.80% similarUnverifiedVKORE's numerical behavior during training on NPUs is consistent with standard training; it is real training, not an approximation.73% similarUnverifiedOpenAI continues to rely on NVIDIA for training while choosing to use its own in-house chips on the inference side.72% similarUnverifiedFeasible paths for hardware-aware training and adaptation mechanisms include: introducing proprioceptive signals such as joint feedback, torque readings, and control frequency as additional input channels into the policy network; performing domain randomization over control parameters, calibration offsets, etc. during training; and introducing online system identification during deployment.72% similarUnverified一个在NVIDIA平台上训练和优化的模型往往需要经过大量工程改造才能运行在其他硬件上72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1027446API
curl https://kongchang.com/api/v1/knowledge/claims/1027446MCP
get_claim(id=1027446)