arXiv:2606.31413v1 Announce Type: cross Abstract: Composing independently trained LoRA adapters into a single large language model is useful for multi-domain adaptation, especially when the original training data cannot be shared. A common approach is to use MoE-style routing over LoRA experts, but for frozen pretrained adapters, soft weighted combinations can change the unit-scale additive update under which each LoRA module was originally trained. We propose \textbf{Hard-Routed MoR-LoRA}, a two-stage framework for composing frozen reasoning LoRA experts through unit-scale hard selection. Fir
The paper addresses current challenges in efficiently composing specialized AI models (LoRAs) while maintaining their individual performance benefits without requiring retraining.
This research provides a method for more effective modular AI development, allowing the integration of domain-specific expertise into large models without the computational cost or data sharing issues of full retraining.
The ability to 'hard-route' and select rather than blend LoRA experts could lead to more robust, interpretable, and computationally efficient composite AI systems.
- · AI developers
- · Cloud providers
- · Enterprises adopting AI
- · Academic AI researchers
- · Inefficient monolithic AI architectures
Increased efficiency and performance in multi-domain large language models through better LoRA composition.
Reduced computational cost and faster iteration for specialized AI applications, fostering broader AI adoption.
Enhanced development of autonomous AI agents capable of flexibly combining diverse reasoning capabilities from pre-trained modules.
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Read at arXiv cs.LG