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cs.LG2025
Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
Ruizhe Wang, Yucheng Ding, Xiao Liu +5
As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the need for efficient training paradigms becomes critical. Despite the vast resources…
cs.LG2025
DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections
Haebin Shin, Lei Ji, Xiao Liu +4
As numerous instruction-tuning datasets continue to emerge, dynamically balancing and optimizing their mixtures has become a critical challenge. To address this, we propose Dynamix…
cs.LG2025
Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training
Kailai Yang, Xiao Liu, Lei Ji +6
Continual pre-training on small-scale task-specific data is an effective method for improving large language models in new target fields, yet it risks catastrophic forgetting of th…