Adam Converges Without Any Modification On Update Rules

• URL: http://arxiv.org/abs/2603.02092v1
• Date: Mon, 02 Mar 2026 17:08:51 GMT
• Title: Adam Converges Without Any Modification On Update Rules
• Authors: Yushun Zhang, Bingran Li, Congliang Chen, Zhi-Quan Luo, Ruoyu Sun,
• Abstract summary: Adam is the default algorithm for training neural networks, including large language models (LLMs).<n>citetreddi 2019convergence provided an example that Adam diverges, raising concerns for its deployment in AI model training.
• Score: 24.855239154362895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adam is the default algorithm for training neural networks, including large language models (LLMs). However, \citet{reddi2019convergence} provided an example that Adam diverges, raising concerns for its deployment in AI model training. We identify a key mismatch between the divergence example and practice: \citet{reddi2019convergence} pick the problem after picking the hyperparameters of Adam, i.e., $(β_1,β_2)$; while practical applications often fix the problem first and then tune $(β_1,β_2)$. In this work, we prove that Adam converges with proper problem-dependent hyperparameters. First, we prove that Adam converges when $β_2$ is large and $β_1 < \sqrt{β_2}$. Second, when $β_2$ is small, we point out a region of $(β_1,β_2)$ combinations where Adam can diverge to infinity. Our results indicate a phase transition for Adam from divergence to convergence when changing the $(β_1, β_2)$ combination. To our knowledge, this is the first phase transition in $(β_1,β_2)$ 2D-plane reported in the literature, providing rigorous theoretical guarantees for Adam optimizer. We further point out that the critical boundary $(β_1^*, β_2^*)$ is problem-dependent, and particularly, dependent on batch size. This provides suggestions on how to tune $β_1$ and $β_2$: when Adam does not work well, we suggest tuning up $β_2$ inversely with batch size to surpass the threshold $β_2^*$, and then trying $β_1< \sqrt{β_2}$. Our suggestions are supported by reports from several empirical studies, which observe improved LLM training performance when applying them.

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