Researchers have proposed a new method for training language agents to balance multiple objectives. The approach, called MINT, involves ranking candidates by their weakest objective rather than using a weighted sum of rewards. This allows the agent to learn more balanced behavior and improve performance in tasks such as emotional support and negotiation. In experiments, MINT was shown to outperform human experts and persist across long interactions. --- Why it matters: This matters because training language agents to balance multiple objectives is a challenging problem that can lead to suboptimal performance. MINT's ability to correct imbalance in proportion to the reference policy's imbalance makes it a promising solution for applications where multiple objectives need to be aligned, such as human-AI collaboration and decision-making. Source: https://arxiv.org/abs/2608.14828