Efficient Estimation of Kernel Surrogate Models for Task Attribution

• URL: http://arxiv.org/abs/2602.03783v1
• Date: Tue, 03 Feb 2026 17:43:48 GMT
• Title: Efficient Estimation of Kernel Surrogate Models for Task Attribution
• Authors: Zhenshuo Zhang, Minxuan Duan, Hongyang R. Zhang,
• Abstract summary: Key question is to quantify how each individual training task influences performance on a target task.<n>The direct approach, leave-one-out retraining, measures the effect of removing each task, but is computationally infeasible at scale.<n>An alternative approach that builds surrogate models to predict a target task's performance for any subset of training tasks has emerged.
• Score: 9.290757451344673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern AI agents such as large language models are trained on diverse tasks -- translation, code generation, mathematical reasoning, and text prediction -- simultaneously. A key question is to quantify how each individual training task influences performance on a target task, a problem we refer to as task attribution. The direct approach, leave-one-out retraining, measures the effect of removing each task, but is computationally infeasible at scale. An alternative approach that builds surrogate models to predict a target task's performance for any subset of training tasks has emerged in recent literature. Prior work focuses on linear surrogate models, which capture first-order relationships, but miss nonlinear interactions such as synergy, antagonism, or XOR-type effects. In this paper, we first consider a unified task weighting framework for analyzing task attribution methods, and show a new connection between linear surrogate models and influence functions through a second-order analysis. Then, we introduce kernel surrogate models, which more effectively represent second-order task interactions. To efficiently learn the kernel surrogate, we develop a gradient-based estimation procedure that leverages a first-order approximation of pretrained models; empirically, this yields accurate estimates with less than $2\%$ relative error without repeated retraining. Experiments across multiple domains -- including math reasoning in transformers, in-context learning, and multi-objective reinforcement learning -- demonstrate the effectiveness of kernel surrogate models. They achieve a $25\%$ higher correlation with the leave-one-out ground truth than linear surrogates and influence-function baselines. When used for downstream task selection, kernel surrogate models yield a $40\%$ improvement in demonstration selection for in-context learning and multi-objective reinforcement learning benchmarks.

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