Scaling Laws for Speculative Decoding

no code implementations • 8 May 2025 • Siyuan Yan, Mo Zhu, Guo-qing Jiang, Jianfei Wang, Jiaxing Chen, Wentai Zhang, Xiang Liao, Xiao Cui, Chen Zhang, Zhuoran Song, Ran Zhu

The escalating demand for efficient decoding in large language models (LLMs) is particularly critical for reasoning-intensive architectures like OpenAI-o3 and DeepSeek-R1, which depend on extended chain-of-thought reasoning.

Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR)

no code implementations • 24 Sep 2023 • Guo-qing Jiang, Jinlong Liu, Zixiang Ding, Lin Guo, Wei Lin

As models for nature language processing (NLP), computer vision (CV) and recommendation systems (RS) require surging computation, a large number of GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput.

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