K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)
arXiv:2607.05903v1 Announce Type: cross Abstract: We present K-ABENA (K-Adaptive Backpropagation with Error-based N-exclusion Algorithm), a selective gradient computation framework that reduces per-iteration training cost by excluding a fraction of low-loss ("minor") observations from the backward pass. Its canonical form (v3) combines a defensive-mixture sampling design over the minor set with Horvitz-Thompson inverse-probability reweighting, yielding a design-unbiased Horvitz-Thompson gradient estimator (Lemma 2) and whose self-normalized practical variant carries a bias of order O(1/m) with
This research addresses fundamental challenges in AI training efficiency that are becoming increasingly critical as model sizes and training data grow exponentially.
Improving the efficiency of AI training reduces computational costs and accelerates development cycles, impacting deployment frequency and accessibility of advanced AI systems.
The proposed method offers a novel approach to optimize backpropagation, potentially leading to faster and more resource-efficient AI model training.
- · AI compute providers
- · Large language model developers
- · AI research institutions
- · Cloud service providers
- · Inefficient AI training methods
- · Energy-intensive AI hardware architectures (without efficiency improvements)
Reduced computational costs for training large AI models.
Faster iteration cycles for AI development and research, leading to quicker innovation.
Democratization of state-of-the-art AI model development by lowering resource barriers.
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Read at arXiv cs.AI