GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
arXiv:2606.14900v1 Announce Type: new Abstract: Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference time, making production deployment infeasible. We propose GRASP (Gradient-Aligned Sequential Parameter Transfer), which achieves superior knowledge integration while maintaining O(1) memory consumption through three key innovations: (1) sequential processing that merges one source at a time into an ev
The rapid increase in AI model complexity and the demand for integrating diverse data sources necessitate more memory-efficient multi-source learning techniques.
This development addresses a critical scalability bottleneck in multi-source transfer learning, potentially enabling more sophisticated and resource-efficient AI deployments in various applications.
Existing approaches requiring O(K) memory for multi-source models can be replaced with methods like GRASP that achieve O(1) memory consumption, making complex model integration feasible.
- · AI researchers and developers
- · Companies deploying multi-modal AI systems
- · Cloud computing providers (reduced memory needs)
- · Edge AI applications
- · Developers reliant on memory-intensive multi-source learning
- · Traditional model fusion techniques
The ability to integrate more source models efficiently will accelerate the development of more robust and generalizable AI systems.
This could lead to a proliferation of AI applications that leverage diverse data without massive computational overhead, including in resource-constrained environments.
Improved memory efficiency might indirectly contribute to lower carbon footprints for large-scale AI training and deployment, as less computational resources are needed.
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Read at arXiv cs.LG