Avoid What You Know: Divergent Trajectory Balance for GFlowNets
arXiv:2602.17827v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a flexible family of amortized samplers trained to generate discrete and compositional objects with probability proportional to a reward function. However, learning efficiency is constrained by the model's ability to rapidly explore diverse high-probability regions during training. To mitigate this issue, recent works have focused on incentivizing the exploration of unvisited and valuable states via curiosity-driven search and self-supervised random network distillation, which tend to waste samples on already well-approximated regions of the state space. In this context, we propose Adaptive Complementary Exploration (ACE), a principled algorithm for the effective exploration of novel and high-probability regions when learning GFlowNets. To achieve this, ACE introduces an exploration GFlowNet explicitly trained to search for high-reward states in regions underexplored by the canonical GFlowNet, whi
arXiv:2602.17827v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a flexible family of amortized samplers trained to generate discrete and compositional objects with probability proportional to a reward function. However, learning efficiency is constrained by the model's ability to rapidly explore diverse high-probability regions during training. To mitigate this issue, recent works have focused on incentivizing the exploration of unvisited and valuable states via curiosity-driven search and self-supervised random network distillation, which tend to waste samples on already well-approximated regions of the state space. In this context, we propose Adaptive Complementary Exploration (ACE), a principled algorithm for the effective exploration of novel and high-probability regions when learning GFlowNets. To achieve this, ACE introduces an exploration GFlowNet explicitly trained to search for high-reward states in regions underexplored by the canonical GFlowNet, which learns to sample from the target distribution. Through extensive experiments, we show that ACE significantly improves upon prior work in terms of approximation accuracy to the target distribution and discovery rate of diverse high-reward states.
Executive Summary
This article proposes Adaptive Complementary Exploration (ACE), a novel algorithm for effective exploration of novel and high-probability regions when learning Generative Flow Networks (GFlowNets). ACE introduces an exploration GFlowNet that searches for high-reward states in underexplored regions, while the canonical GFlowNet learns to sample from the target distribution. Through extensive experiments, ACE demonstrates improved approximation accuracy and discovery rate of diverse high-reward states compared to prior work. The article contributes to the development of more efficient and effective GFlowNets, which have applications in various domains such as computer vision and natural language processing. The proposed algorithm is well-suited for scenarios where exploration-exploitation trade-offs are critical, and the ability to efficiently discover high-reward states is essential.
Key Points
- ▸ ACE introduces an exploration GFlowNet to search for high-reward states in underexplored regions
- ▸ The canonical GFlowNet learns to sample from the target distribution
- ▸ ACE demonstrates improved approximation accuracy and discovery rate of diverse high-reward states
Merits
Strength in Exploration
ACE effectively balances exploration and exploitation by introducing an exploration GFlowNet, which efficiently searches for high-reward states in underexplored regions
Improved Accuracy
ACE demonstrates improved approximation accuracy to the target distribution compared to prior work
Increased Diversity
ACE increases the discovery rate of diverse high-reward states, enabling more efficient exploration of the state space
Demerits
Computational Complexity
ACE may introduce additional computational complexity due to the training of the exploration GFlowNet and the need for parallel exploration and exploitation
Hyperparameter Tuning
ACE requires careful tuning of hyperparameters to balance exploration and exploitation, which can be challenging in practice
Expert Commentary
The proposed ACE algorithm is a significant contribution to the development of more efficient and effective GFlowNets. By introducing an exploration GFlowNet, ACE effectively balances exploration and exploitation, leading to improved approximation accuracy and discovery rate of diverse high-reward states. However, the additional computational complexity and need for careful hyperparameter tuning are potential limitations. Future work should focus on addressing these limitations and exploring the application of ACE in various domains.
Recommendations
- ✓ Further research is needed to address the computational complexity and hyperparameter tuning challenges associated with ACE
- ✓ ACE should be applied to various domains to demonstrate its effectiveness and versatility
