Budgeted LoRA: Structured Compute Allocation for Efficient LLM Inference
A recent paper published on arXiv introduces Budgeted LoRA, a framework for distilling large language models by framing model compression as a structured problem of compute allocation. In contrast to earlier methods like LoRA that lower adaptation costs while keeping the dense backbone intact, Budgeted LoRA establishes a global compute budget that determines the desired proportion of dense computation to retain. This framework enables the model to reallocate capacity between dense and low-rank pathways using three strategies: module-level dense retention coefficients, adaptive low-rank allocation, and post-training compression that selectively modifies, approximates, or maintains dense components. The aim is to create student models that are both cost-effective to train and efficient during inference. The paper can be found on arXiv with the identifier 2605.04341.
Key facts
- Budgeted LoRA is a distillation framework for large language models.
- It treats model compression as a structured compute allocation problem.
- A global compute budget sets the final target fraction of dense computation retained.
- Three mechanisms: module-level dense retention coefficients, adaptive low-rank allocation, post-training compression.
- Aims to produce student models structurally efficient at inference time.
- Prior approaches like LoRA reduce adaptation cost but leave dense backbone unchanged.
- Paper available on arXiv: 2605.04341.
Entities
Institutions
- arXiv