Uncovering Cross-Objective Interference in Multi-Objective Alignment
This addresses a persistent failure mode in aligning LLMs with multiple objectives, which is crucial for developing safer and more reliable AI systems, though it is incremental as it builds on existing scalarization methods.
The paper tackles the problem of cross-objective interference in multi-objective alignment for large language models, where training improves some objectives while degrading others, and proposes Covariance Targeted Weight Adaptation (CTWA) to mitigate this issue, showing it effectively maintains positive covariance between objective rewards and training signals.
We study a persistent failure mode in multi-objective alignment for large language models (LLMs): training improves performance on only a subset of objectives while causing others to degrade. We formalize this phenomenon as cross-objective interference and conduct the first systematic study across classic scalarization algorithms, showing that interference is pervasive and exhibits strong model dependence. To explain this phenomenon, we derive a local covariance law showing that an objective improves at first order when its reward exhibits positive covariance with the scalarized score. We extend this analysis to clipped surrogate objectives used in modern alignment, demonstrating that the covariance law remains valid under mild conditions despite clipping. Building on this analysis, we propose Covariance Targeted Weight Adaptation (CTWA), a plug-and-play method that maintains positive covariance between objective rewards and the training signal to effectively mitigate cross-objective interference. Finally, we complement these local improvement conditions with a global convergence analysis under the Polyak--Łojasiewicz condition, establishing when non-convex scalarized optimization achieves global convergence and how cross-objective interference depends on specific model geometric properties.