Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv:2602.21565v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial. Further extending GFlowNets to multi-objective settings has attracted growing interest since real-world applications often involve multiple, conflicting objectives. However, existing approaches require additional training for each set of objectives, limiting their applicability and incurring substantial computational overhead. We propose a training-free mixing policy that composes pre-trained GFlowNets at inference time, enabling rapid adaptation without finetuning or retraining. Importantly, our framework is flexible, capable of handling diverse reward combinations ranging from linear scalarization to complex non-linear logical operators, which are often handled separately in previous literature. We prove that our method exac
arXiv:2602.21565v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial. Further extending GFlowNets to multi-objective settings has attracted growing interest since real-world applications often involve multiple, conflicting objectives. However, existing approaches require additional training for each set of objectives, limiting their applicability and incurring substantial computational overhead. We propose a training-free mixing policy that composes pre-trained GFlowNets at inference time, enabling rapid adaptation without finetuning or retraining. Importantly, our framework is flexible, capable of handling diverse reward combinations ranging from linear scalarization to complex non-linear logical operators, which are often handled separately in previous literature. We prove that our method exactly recovers the target distribution for linear scalarization and quantify the approximation quality for nonlinear operators through a distortion factor. Experiments on a synthetic 2D grid and real-world molecule-generation tasks demonstrate that our approach achieves performance comparable to baselines that require additional training.
Executive Summary
The article proposes a novel approach to composing pre-trained Generative Flow Networks (GFlowNets) for multi-objective generation without requiring additional training. This method enables rapid adaptation to diverse reward combinations, including linear scalarization and complex non-linear logical operators. The framework is flexible and achieves performance comparable to baselines that require additional training, making it a significant contribution to the field of scientific discovery and multi-objective optimization.
Key Points
- ▸ Training-free composition of pre-trained GFlowNets
- ▸ Rapid adaptation to diverse reward combinations
- ▸ Flexibility in handling linear and non-linear operators
Merits
Efficient Adaptation
The proposed method allows for efficient adaptation to new objectives without requiring additional training, reducing computational overhead and increasing applicability.
Demerits
Limited Theoretical Guarantees
The method only provides exact recovery guarantees for linear scalarization, and the distortion factor for non-linear operators may limit its performance in certain scenarios.
Expert Commentary
The article presents a significant advancement in the field of GFlowNets, enabling efficient and flexible composition of pre-trained models for multi-objective generation. The theoretical guarantees and experimental results demonstrate the potential of this approach to accelerate scientific discovery and optimization. However, further research is necessary to address the limitations and extend the method to more complex scenarios. The implications of this work are far-reaching, with potential applications in various domains and contributions to the development of more efficient AI technologies.
Recommendations
- ✓ Further investigation into the theoretical guarantees for non-linear operators
- ✓ Exploration of the proposed method in additional domains and applications
