Adaptive Head Budgeting for Efficient Multi-Head Attention
Multi-head attention enables Transformers to capture diverse representations, but all attention heads are typically activated for every input, regardless of task complexity. For coarse-grained tasks such as text classification, where relevant information is often global, this fixed allocation can introduce unnecessary computation. We propose BudgetFormer, a Transformer architecture that dynamically allocates attention heads on a per-input basis. The model learns both a head budget and a relevance distribution to select the most informative heads. To support effective head selection, we introduce a training strategy that balances exploration and exploitation. Experiments on text classifica...
| Authors | Bilal Faye, Abdoulaye Mbaye, Hanane Azzag et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 April 2026 |
| Last updated at source | 4 June 2026 |
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