arXiv:2601.17257v2 Announce Type: replace Abstract: We introduce a constrained optimization framework for training transformers that behave like optimization descent algorithms. Specifically, we enforce layerwise descent constraints on the objective function and replace standard empirical risk minimization (ERM) with a primal-dual training scheme. This approach yields models whose intermediate representations decrease the loss monotonically in expectation across layers. We apply our method to both unrolled transformer architectures and conventional pretrained transformers on tasks of video den
The paper provides a new conceptual framework for transformer training, building on recent advances and continued research into optimizing large language models.
This new optimization approach could lead to more efficient and stable training of advanced AI models, potentially accelerating progress in AI capabilities and reducing computational costs.
By enforcing layerwise descent constraints and replacing ERM with a primal-dual scheme, training could become more predictable and robust, resulting in models that decrease loss monotonically.
- · AI researchers
- · Deep learning developers
- · Cloud computing providers
- · Inefficient AI training methods
More stable and potentially faster training of complex neural network architectures like transformers.
Reduced computational resource requirements for achieving high-performing AI models, democratizing access to advanced AI development.
Accelerated development of more capable and reliable AI systems across various applications, from video analysis to language understanding.
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Read at arXiv cs.LG