Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training
arXiv:2602.00747v3 Announce Type: replace-cross Abstract: Determining an effective data mixture is a key factor in Large Language Model (LLM) pre-training, where models must balance general competence with proficiency on hard tasks such as math and code. However, identifying an optimal mixture remains an open challenge, as existing approaches either rely on unreliable tiny-scale proxy experiments or require prohibitively expensive large-scale exploration. To address this, we propose Decouple Searching from Training Mix (DeMix), a novel framework that leverages model merging to predict optimal
The increasing scale and complexity of Large Language Models necessitate more efficient data mixing strategies to optimize pre-training costs and performance.
Optimizing data mixtures is crucial for LLM development, directly impacting the cost, efficiency, and capabilities of future AI systems.
The proposed DeMix framework offers a way to decouple data searching from training, potentially making the development of high-performing LLMs more accessible and less resource-intensive.
- · AI model developers
- · Cloud computing providers
- · Research institutions
- · Startups building specialized LLMs
- · Companies with inefficient LLM development pipelines
More efficient and cost-effective LLM pre-training becomes possible, accelerating research and development.
A broader range of organizations may be able to develop advanced LLMs due to reduced computational requirements for data optimization.
Increased competition and innovation in the LLM space could lead to more diverse and powerful AI applications across various industries.
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Read at arXiv cs.AI