Transformers are Deep Optimizers: Provable In-Context Learning for Deep Model Training

• URL: http://arxiv.org/abs/2411.16549v1
• Date: Mon, 25 Nov 2024 16:32:11 GMT
• Title: Transformers are Deep Optimizers: Provable In-Context Learning for Deep Model Training
• Authors: Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu, Zhao Song, Han Liu,
• Abstract summary: We investigate the capability for in-context learning (ICL) to simulate the training process of deep models. Specifically, we provide an explicit construction of a $(2N+4)L$-layer transformer capable of simulating $L$ gradient descent steps. We validate our findings on synthetic datasets for 3-layer, 4-layer, and 6-layer neural networks.
• Score: 11.940454262201161
• License:
• Abstract: We investigate the transformer's capability for in-context learning (ICL) to simulate the training process of deep models. Our key contribution is providing a positive example of using a transformer to train a deep neural network by gradient descent in an implicit fashion via ICL. Specifically, we provide an explicit construction of a $(2N+4)L$-layer transformer capable of simulating $L$ gradient descent steps of an $N$-layer ReLU network through ICL. We also give the theoretical guarantees for the approximation within any given error and the convergence of the ICL gradient descent. Additionally, we extend our analysis to the more practical setting using Softmax-based transformers. We validate our findings on synthetic datasets for 3-layer, 4-layer, and 6-layer neural networks. The results show that ICL performance matches that of direct training.

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