Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models
Fine-tuning instruct models often improves target performance while inducing behavioral drift from the reference model, which can degrade existing capabilities. Rather than treating this drift as an uncontrolled consequence of optimization, we specify a behavioral drift budget before optimization and ask how to boost the target-task performance within it. Locally, behavioral dr
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Fine-tuning instruct models often improves target performance while inducing behavioral drift from the reference model, which can degrade existing capabilities. Rather than treating this drift as an uncontrolled consequence of optimization, we specify a behavioral drift budget before optimization and ask how to boost the target-task performance within it. Locally, behavioral dr
This research offers a novel perspective on fine-tuning by explicitly managing behavioral drift, a critical challenge in deploying specialized models. ML engineers can leverage this framework to develop more robust and predictable fine-tuning strategies, ensuring models retain general capabilities while improving on specific tasks.