# ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodings

arXiv: https://arxiv.org/abs/2509.00280 · HTML: https://mlsanity.com/paper/2509.00280/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Algorithms with Predictions](https://mlsanity.com/paper/2006.09123/) (2020)
- [Tensor Methods in Computer Vision and Deep Learning](https://mlsanity.com/paper/2107.03436/) (2021)
- [Static Neural Compiler Optimization via Deep Reinforcement Learning](https://mlsanity.com/paper/2008.08951/) (2020)
- [Tensor Decomposition for Model Reduction in Neural Networks: A Review](https://mlsanity.com/paper/2304.13539/) (2023)
- [Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation](https://mlsanity.com/paper/1905.12799/) (2019)
- [Load-Balanced Sparse MTTKRP on GPUs](https://mlsanity.com/paper/1904.03329/) (2019)
- [ALTO: Adaptive Linearized Storage of Sparse Tensors](https://mlsanity.com/paper/2102.10245/) (2021)
- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)
- [Efficient, Out-of-Memory Sparse MTTKRP on Massively Parallel Architectures](https://mlsanity.com/paper/2201.12523/) (2022)
- [Optimizing Sparse Linear Algebra Through Automatic Format Selection and Machine Learning](https://mlsanity.com/paper/2303.05098/) (2023)
- [Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation](https://mlsanity.com/paper/2403.06348/) (2024)

## Abstract (arXiv)

Tensor decomposition (TD) is essential for analyzing high-dimensional sparse data, yet its irregular computations and memory-access patterns pose major performance challenges on modern parallel processors. Prior works rely on expert-designed sparse tensor formats that fail to adapt to irregular tensor shapes and data distributions. We present the reinforcement-learned adaptive tensor encoding (ReLATE) framework, a learning-augmented method that discovers safe and efficient sparse encodings, without labeled examples, via a hybrid model-free and model-based algorithm that learns from both real and imagined actions. Moreover, ReLATE introduces elastic training, rule-driven action masking, and dynamics-informed action filtering to ensure correct encoding with bounded execution time, even during early learning. After offline training, with geometric-mean overhead of only 5.82% relative to TD workflow time, ReLATE deploys the best encoding with zero inference overhead. Across diverse real-world sparse tensors, ReLATE consistently outperforms the best expert-designed format by up to 2x, with a geometric-mean speedup of 1.38-1.41x.