A new AI training method targets a specific failure mode: language agents trained on several objectives at once usually optimize the cheap one and quietly ignore the rest.
Researchers describe Mint (MIN-selection preference disTillation) as a one-line change to a standard technique called preference distillation. Instead of ranking candidate responses by a weighted sum of reward scores, Mint ranks them by whichever objective scored worst, then trains the model with an otherwise unchanged DPO objective. Tested on two tasks - cooperative emotional support and adversarial negotiation - the method lifted the weaker objective's score from 0.37 to 0.64, a statistically significant jump (p < 10^-40) that beat human experts on that axis. The gain held up across full multi-turn conversations, not just single exchanges.
The finding cuts at a real problem in preference-based training: additive rewards have no concept of balance, so a support agent can learn to sound warm while giving no actual help, and standard optimization has no reason to notice. Because Mint changes only how candidates are ranked, it should be simple to bolt onto existing DPO pipelines without redesigning the reward model.
The catch is that a "one-line change" claim is easy to make and hard to generalize - this is two benchmark tasks in one paper, not a proven fix for every multi-objective alignment problem.