Drift-Constrained Optimization for Instruct Model Fine-Tuning
September 11, 2026
Fine-tuning is reformulated as a direction-selection problem to mitigate behavioral drift from reference models. By specifying a behavioral drift budget, practitioners can maximize target-task performance while remaining within a predefined geometric boundary of the original model's capabilities.
HOW THIS AFFECTS YOU
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builderYou can now set explicit budgets to prevent fine-tuning from destroying your model's base capabilities.
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researcherThis provides a formal geometric framework for studying the trade-offs in instruction tuning.