Unverified50% confidenceFactExact time
KS uses spline blocks to construct a piecewise-smooth manifold, thereby reducing the number of iterations needed for training while maintaining expressiveness.
1
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50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
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Related Claims
UnverifiedThe training framework may adopt a reduction strategy with cross-tensor-parallel sharding for throughput, while the inference framework uses a different kernel in KV-cache optimization mode; even with identical inputs, the last few ULPs of the output may differ.64% similarUnverifiedModern training frameworks use mixed-precision training and gradient scaling (loss scaling) techniques to prevent small values from being swallowed by amplifying small gradients into the representable range.63% similarUnverifiedMoE training requires an additional load-balancing loss to prevent routing collapse, and DeepSeek-MoE adopts a finer-grained expert partitioning strategy.63% similarUnverifiedBy learning the mapping from geometry to value field from noisy, partially converged Monte Carlo labels, PUN amortizes computation into the training phase, avoiding expensive convergence for every sample.63% similarUnverifiedIn distributed training, a small number of nodes whose kernel parameters are inconsistent with the rest can drag down the throughput of the entire training job, creating bottleneck nodes.62% similar
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