[论文] Which Objectives Need a Dial? Predicting Objective Conflict and Coveri...

论文概要

研究领域: NLP
作者: David Tsoi, Esra Dönmez
发布时间: 2026-09-25
arXiv: 2609.26929

中文摘要

人们持有多元、有时相互冲突的价值观，任何单一对齐模型都无法满足所有人。多元对齐因此需要可转向的模型，能以不同方式平衡相互竞争的目标。多目标直接偏好优化（MODPO）通过目标权重张成权衡的连续谱。本文研究两个问题：一个模型何时能同时改进两个目标？如何在不训练多个模型的情况下覆盖多种权衡？在来自 HelpSteer 与 UltraFeedback 的七对目标上，两项预训练测量能预测目标在人类标注数据上是对齐还是冲突，但在 AI 标注数据上不行——后者中回复长度与重复度混淆了奖励模型分数。在更广的权衡覆盖上，选择最近的已训练模型与合并模型参数都有帮助，但都无法稳定匹敌直接训练。这些发现为构建服务多元偏好的可转向模型提供了实用指导。

原文摘要

People hold diverse, sometimes conflicting values, so no single aligned model can satisfy everyone. Pluralistic alignment therefore calls for steerable models that can balance competing objectives differently. Multi-Objective Direct Preference Optimization (MODPO) does this by using an objective weight to span a continuum of trade-offs. We study two questions: when can one model improve two objectives simultaneously, and how can many trade-offs be covered without training a separate model for each? Across seven objective pairs from HelpSteer and UltraFeedback, two pre-training measurements predict whether objectives align or conflict for human-annotated data, but not for AI-annotated data, where response length and repetition confound reward-model scores. For broader trade-off coverage, se...

自动采集于 2026-09-25

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