Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization
This work addresses a specific bottleneck in GFlowNets for researchers in generative modeling, offering an incremental improvement over existing methods.
The paper tackled the limitation of fixed backward policies in GFlowNets by introducing a backward policy optimization algorithm that maximizes trajectory likelihood, demonstrating faster convergence and improved mode discovery in complex environments.
Generative Flow Networks (GFlowNets) are a family of generative models that learn to sample objects with probabilities proportional to a given reward function. The key concept behind GFlowNets is the use of two stochastic policies: a forward policy, which incrementally constructs compositional objects, and a backward policy, which sequentially deconstructs them. Recent results show a close relationship between GFlowNet training and entropy-regularized reinforcement learning (RL) problems with a particular reward design. However, this connection applies only in the setting of a fixed backward policy, which might be a significant limitation. As a remedy to this problem, we introduce a simple backward policy optimization algorithm that involves direct maximization of the value function in an entropy-regularized Markov Decision Process (MDP) over intermediate rewards. We provide an extensive experimental evaluation of the proposed approach across various benchmarks in combination with both RL and GFlowNet algorithms and demonstrate its faster convergence and mode discovery in complex environments.