Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning
arXiv:2507.12612v3 Announce Type: replace Abstract: Supervised fine-tuning performance for large language models depends strongly on how training budget is distributed across a heterogeneous set of tasks. In practice, mixtures are often fixed using simple heuristics (e.g., uniform or size-proportional sampling) that ignore task interactions, which can hurt transfer and waste budget on redundant sources. We introduce TaskPGM, a framework for learning continuous task mixtures via an energy-based model over tasks. Tasks form the nodes of a Markov random field: unary potentials capture per-task ut
The increasing scale and complexity of large language models necessitate more efficient and effective fine-tuning methods to maximize performance and minimize computational waste.
Optimizing model fine-tuning with probabilistic task selection directly impacts the cost, performance, and accessibility of advanced AI systems, influencing technological leadership and economic competitiveness.
The approach to fine-tuning large language models shifts from heuristic task sampling to a more data-driven, mutual information-based method, potentially improving model efficiency and transfer learning.
- · AI developers
- · Cloud providers with compute resources
- · Companies deploying custom LLMs
- · Organizations with inefficient LLM fine-tuning pipelines
- · Developers relying on suboptimal heuristics
Improved performance and reduced computational costs for fine-tuning large language models.
Accelerated development and deployment of more specialized and capable AI agents across diverse applications.
Enhanced AI capabilities leading to new product categories and increased market dominance for companies leveraging these advanced fine-tuning techniques.
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Read at arXiv cs.LG