ALTO accelerates LoRA tuning up to 13.8x by monitoring loss trajectories for early stopping, using fused grouped GEMM with rank-local adapter parallelism, and combining intra- and inter-task scheduling for heterogeneous workloads without quality loss.
Early stopping-but when? InNeural Networks: Tricks of the trade, pages 55–69
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
STAR cuts P99 TPOT by 75.1% and raises goodput 2.63x via a lightweight hidden-state length predictor and dynamic decode rescheduling that combines current and predicted loads.
citing papers explorer
-
ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads
ALTO accelerates LoRA tuning up to 13.8x by monitoring loss trajectories for early stopping, using fused grouped GEMM with rank-local adapter parallelism, and combining intra- and inter-task scheduling for heterogeneous workloads without quality loss.
-
A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
-
STAR: Decode-Phase Rescheduling for LLM Inference
STAR cuts P99 TPOT by 75.1% and raises goodput 2.63x via a lightweight hidden-state length predictor and dynamic decode rescheduling that combines current and predicted loads.