Which Objectives Need a Dial? Predicting Objective Conflict and Covering Trade-offs in Steerable Pluralistic Alignment

A study on predicting objective conflict and covering trade-offs in multi-objective preference optimization

David Tsoi; Esra Dönmez· 2026· DOI 10.48550/arXiv.2609.26929

The core problem

Pluralistic alignment recognizes that people hold diverse and sometimes conflicting values, making it impossible for a single aligned model to satisfy everyone. Steerable models, which can balance competing objectives differently, are therefore essential. Multi-Objective Direct Preference Optimization (MODPO) addresses this by using an objective weight to span a continuum of trade-offs. This paper studies two key questions: (1) When can one model improve two objectives simultaneously? (2) How can many trade-offs be covered without training a separate model for each? The authors analyze seven objective pairs from the HelpSteer and UltraFeedback datasets, examining both human-annotated and AI-annotated data. They find that 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, they explore selecting the nearest trained model and merging model parameters, finding that both help but neither consistently matches direct training. These findings provide practical guidance for building steerable models that serve div

Across seven objective pairs, the authors find that two pre-training measurements—preference data correlation and reward model agreement—predict whether objectives align or conflict for human-annotated data. Specifically, when these measurements indicate alignment, a single model can improve both objectives simultaneously; when they indicate conflict, trade-offs are necessary. However, for AI-annotated data, these measurements fail to predict conflict due to confounding factors: response length and repetition inflate reward-model scores, leading to spurious correlations. For trade-off coverage, selecting the nearest trained model and merging model parameters both improve over a single model, but neither consistently matches direct training at the target weight. The performance gap varies by objective pair and weight, with merging sometimes outperforming nearest-model selection and vice versa. The authors also note that direct training remains the most reliable method for achieving optimal trade-offs, though it is computationally expensive.

Pluralistic alignment recognizes that people hold diverse and sometimes conflicting values, making it impossible for a single aligned model to satisfy everyone. Steerable models, which can balance competing objectives differently, are therefore essential. Multi-Objective Direct Preference Optimization (MODPO) addresses this by using an objective weight to span a continuum of trade-offs. This paper studies two key questions: (1) When can one model improve two objectives simultaneously? (2) How can many trade-offs be covered without training a separate model for each? The authors analyze seven objective pairs from the HelpSteer and UltraFeedback datasets, examining both human-annotated and AI-annotated data. They find that 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, they explore selecting the nearest trained model and merging model parameters, finding that both help but neither consistently matches direct training. These findings provide practical guidance for building steerable models that serve diverse preferences.

The authors employ Multi-Objective Direct Preference Optimization (MODPO), which extends Direct Preference Optimization (DPO) to multiple objectives by introducing an objective weight vector. The MODPO loss for a single objective is given by:

The findings have important implications for pluralistic alignment. First, the two pre-training measurements can guide practitioners in deciding whether to invest in multi-objective training or to expect inherent trade-offs. For human-annotated data, these measurements are reliable predictors, but for AI-annotated data, they are confounded by length and repetition, suggesting that reward models for AI data may need debiasing. Second, the trade-off coverage strategies—nearest-model selection and parameter merging—offer practical alternatives to training many models, but their inconsistent performance means that direct training may still be necessary for critical applications. The authors suggest that future work could explore better merging techniques or adaptive weight selection. Overall, this work provides a foundation for building steerable models that can serve diverse preferences without exhaustive training.

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