arXiv:2606.01717v1 Announce Type: new Abstract: Instruction tuning aligns large language models, including multimodal ones, with diverse user intents, but scaling to heterogeneous mixtures is hindered by gradient interference and bandwidth-heavy synchronization. We ask whether these two bottlenecks can be addressed jointly by training parts of the mixture independently and reconciling them once in parameter space. We develop a local quadratic theory inside a shared flat basin that yields three results: weight merging produces a curvature-weighted variance reduction; PCA-aligned conflict splitt
The proliferation of various large language models and multimodal AI necessitates more efficient and scalable training methods to address issues like gradient interference and bandwidth constraints.
This research offers a potential solution to significant bottlenecks in scaling instruction tuning for large language models, impacting the development and deployment of advanced AI systems.
The proposed method of decentralized instruction tuning and weight merging could enable more efficient training of heterogenous AI mixtures, reducing resource demands and accelerating model development.
- · AI developers
- · Cloud computing providers (through efficiency gains)
- · Researchers in distributed AI
- · Companies relying on less efficient centralized training paradigms
More diverse and capable AI models can be trained and deployed faster due to increased efficiency.
Reduced computational costs for specific AI training tasks could democratize access to advanced model development.
This could accelerate the development of more complex AI agents by providing a clearer path to integrate diverse functionalities efficiently.
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Read at arXiv cs.LG