Managing Conflicting Objectives in Generative Flow Models

When training generative models using compositional rewards—where multiple objective functions are combined to steer the model toward specific outcomes—a common failure point is the emergence of conflicting gradients. When different reward signals pull the model in opposing directions, standard additive guidance often leads to suboptimal samples or 'reward hacking,' where the model prioritizes one objective at the expense of others.

The Conflict-Aware Guidance Mechanism

The authors propose a novel framework for flow models that explicitly detects and mitigates these conflicts during the inference or fine-tuning process. Instead of applying a static sum of reward gradients, the model evaluates the alignment of gradients from different reward components. By identifying when gradients are in opposition, the system applies a dynamic weighting or projection mechanism that suppresses the conflicting components. This ensures that the model maintains a balanced adherence to all desired constraints rather than collapsing into a single, dominant reward signal.

Practical Implications for AI Alignment

This approach is particularly relevant for applications requiring high-precision control, such as robotics or complex image synthesis, where multiple constraints (e.g., safety, aesthetic quality, and task-specific accuracy) must be satisfied simultaneously. By moving away from naive additive guidance, this method allows for more stable steerability in flow-based architectures, reducing the need for extensive manual tuning of reward weights. The research, presented at ICML 2026, provides a mathematical foundation for ensuring that complex, multi-objective prompts do not result in model divergence.