Mixture-Trained Merging for Unified Multi-Objective Models

Abstract

Unified language models are increasingly expected to combine heterogeneous capabilities, such as mathematics, code, instruction following, and controllable thinking behavior, within a single set of parameters. A common solution is sequential post-training on multiple objectives, but this entangles all objectives along one optimization trajectory and makes the final model highly sensitive to training order, data ratios, schedules, and stopping criteria. Weight-space merging offers a modular alternative, but naive merging of single-objective experts often fails: domain capabilities degrade sharply, or think/non-think modes collapse into one dominant behavior. We attribute both failures to incompatible weight-space geometry: experts trained on single objectives drift to distant regions of parameter space, placing their interpolations outside any shared low-loss basin. We propose Mixture-Trained Merging (MTM), which trains each branch on an objective-biased data mixture rather than a single objective, exposing it to cross-objective interactions and making branches compatible at merge time. MTM uses merged-model evaluations as a low-cost signal for selecting branch mixtures, avoiding expensive data-mixture ablations. The procedure is iterative: each round promotes the base model using globally selected merge coefficients and refines each branch mixture using domain-preferred coefficients under constraints that preserve other objectives. To scale beyond simplex grid search, MTM uses qNEHVI-based multi-objective Bayesian optimization. Across code, mathematics, instruction following, and think/non-think control, MTM outperforms naive merging and preserves behavioral separation where single-objective merging collapses, suggesting that effective unified models require branches trained to be mergeable.