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arXiv · 2504.16968

BackSlash: Rate Constrained Optimized Training of Large Language Models

Jun Wu·Jiangtao Wen·Yuxing Han
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Abstract

The rapid advancement of large-language models (LLMs) has driven extensive research into parameter compression after training has been completed, yet compression during the training phase remains largely unexplored. In this work, we introduce Rate-Constrained Training (BackSlash), a novel training-time compression approach based on rate-distortion optimization (RDO). BackSlash enables a flexible trade-off between model accuracy and complexity, significantly reducing parameter redundancy while preserving performance. Experiments in various architectures and tasks demonstrate that BackSlash can reduce memory usage by 60% - 90% without accuracy loss and provides significant compression gain compared to compression after training. Moreover, BackSlash proves to be highly versatile: it enhances generalization with small Lagrange multipliers, improves model robustness to pruning (maintaining accuracy even at 80% pruning rates), and enables network simplification for accelerated inference on edge devices.

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Jun Wu, Jiangtao Wen, Yuxing Han. 2025-04-23. BackSlash: Rate Constrained Optimized Training of Large Language Models. https://arxiv.org/abs/2504.16968

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