Title: Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training
Primary area: reinforcement learning
Keywords: Generative Flow Networks (GFlowNets), proxy-free guidance

Abstract: Generative Flow Networks (GFlowNets) are effective at sampling diverse, high-reward objects, but in many real-world settings where new reward queries are infeasible, they must be trained from offline datasets. The prevailing proxy-based training methods are susceptible to error propagation, while existing proxy-free approaches often use coarse constraints that limit exploration. To address these issues, we propose **Trajectory-Distilled GFlowNet (TD-GFN)**, a novel proxy-free training framework. TD-GFN learns dense, transition-level edge rewards from offline trajectories via inverse reinforcement learning to provide rich structural guidance for efficient exploration. Crucially, to ensure robustness, these rewards are used indirectly to guide the policy through DAG pruning and prioritized backward sampling of training trajectories. This ensures that final gradient updates depend only on ground-truth terminal rewards from the dataset, thereby preventing the error propagation. Experiments show that TD-GFN significantly outperforms a broad range of existing baselines in both convergence speed and final sample quality, establishing a more robust and efficient paradigm for offline GFlowNet training.

TL;DR: We propose a proxy-free framework for offline GFlowNet training that learns dense guidance from trajectories via IRL, steering exploration through DAG pruning and prioritized sampling for remarkable efficiency and performance.