APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation

Javier Marín

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arXiv · 2505.19912

APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation

Javier Marín

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Abstract

We present Adjacent Possible Exploration (APE), a selective fine-tuning method for adapting large language models that systematically explores parameter modifications while maintaining model stability. Inspired by evolutionary optimization principles, APE evaluates multiple candidate parameter updates through fine-tuning on small data subsets and accepts only those exceeding a performance threshold. Unlike standard fine-tuning that follows single gradient directions, APE implements a filtered selection process that prevents destabilizing parameter changes while enabling systematic improvement. Our method achieves 33.9\% BLEU improvement and 36.2\% perplexity reduction on news summarization tasks while using minimal computational resources. The approach provides a practical framework for controlled model adaptation that balances performance gains with representational stability.

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Javier Marín. 2025-05-26. APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation. https://arxiv.org/abs/2505.19912

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