Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning
📰 ArXiv cs.AI
Learn to optimize model fine-tuning by selecting tasks via mutual information, improving transfer learning and reducing training budget waste
Action Steps
- Define a set of tasks for fine-tuning using TaskPGM framework
- Calculate mutual information between tasks to identify interactions and dependencies
- Learn a continuous task mixture using the calculated mutual information
- Apply the learned task mixture to fine-tune a large language model
- Evaluate the performance of the fine-tuned model on a target task
Who Needs to Know This
ML engineers and researchers can benefit from this approach to optimize their model fine-tuning pipelines and improve overall performance
Key Insight
💡 Mutual information can be used to select tasks for fine-tuning, improving transfer learning and reducing training budget waste
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Optimize model fine-tuning with TaskPGM, a framework for probabilistic task selection via mutual information #ML #FineTuning
Key Takeaways
Learn to optimize model fine-tuning by selecting tasks via mutual information, improving transfer learning and reducing training budget waste
Full Article
Title: Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning
Abstract:
arXiv:2507.12612v3 Announce Type: replace-cross 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-b
Abstract:
arXiv:2507.12612v3 Announce Type: replace-cross 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-b
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