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Research questionHow can idle inference resources reduce scarce-GPU training cost without biasing gradient estimates?Training can face scarce, expensive GPU forwards even when lower-cost inference capacity is idle. Approximate gradients could reduce that bottleneck, but their errors may distort optimization if they introduce bias.
AI
Inference Optimization
LLM Pretraining & Post-training
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Gradient Prediction with Control Variates in the Cheap-Forward RegimeThe evidence uses a simulated compute ledger in which fleet work costs a fraction of a scarce-GPU forward; all experiments run on a regular GPU. It evaluates reduced-precision inference-style gradient predictions combined with a few exact gradients on a 124M-parameter language model over selected short training windows, with additional results spanning 10M–774M parameters. Inference-only hardware, end-to-end distributed latency, and a full optimizer-by-batch-size baseline sweep are not tested.research paper · Sep 2, 2026
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