New Framework Predicts LLM Fine-Tuning Performance to Reduce Costs

Fine-tuning large language models (LLMs) is a computationally expensive process, making pre-hoc performance prediction a valuable tool for cost reduction. This study explores the theoretical boundaries of such predictions by framing it as a stochastic estimation problem with information constraints. The researchers decompose prediction risk into two main parts: an intrinsic limit related to static data-model compatibility and a reducible optimization variance. They demonstrate that this optimization variance has a fundamental lower bound on its decay rate, which imposes limits on how quickly prediction uncertainty can be reduced, irrespective of the prediction method used. Based on these insights, the work proposes a budget-optimal probing principle and a predictability phase diagram. This diagram categorizes tasks into three regimes: Static-Sufficient, Dynamic-Critical, and Noise-Dominant. Experimental validation on both synthetic and real-world datasets confirms these theoretical regimes and highlights the efficiency of their probing strategy.