BoostTransformer: Enhancing Transformer Models with Subgrid Selection and Importance Sampling

• URL: http://arxiv.org/abs/2508.02924v1
• Date: Mon, 04 Aug 2025 21:54:16 GMT
• Title: BoostTransformer: Enhancing Transformer Models with Subgrid Selection and Importance Sampling
• Authors: Biyi Fang, Jean Utke, Truong Vo, Diego Klabjan,
• Abstract summary: BoostTransformer augments transformers with boosting principles through subgrid token selection and importance-weighted sampling.<n>Our method incorporates a least square boosting objective directly into the transformer pipeline, enabling more efficient training and improved performance.
• Score: 11.246174442827282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer architectures dominate modern NLP but often demand heavy computational resources and intricate hyperparameter tuning. To mitigate these challenges, we propose a novel framework, BoostTransformer, that augments transformers with boosting principles through subgrid token selection and importance-weighted sampling. Our method incorporates a least square boosting objective directly into the transformer pipeline, enabling more efficient training and improved performance. Across multiple fine-grained text classification benchmarks, BoostTransformer demonstrates both faster convergence and higher accuracy, surpassing standard transformers while minimizing architectural search overhead.

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