通过分块探索加速大状态空间下的生成收敛
Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
- 将状态空间划分为重叠子区域，限制生成器的探索范围
- 在多个数据集上收敛速度更快，且生成样本奖励更高、更多样
- 适合需要高效探索大空间的生成任务，如结构设计与分子生成
生成流网络（GFlowNets）在按奖励比例生成高分候选方面展现出潜力。然而，现有GFlowNets在大规模状态空间中自由探索时面临显著收敛挑战。本文提出通过规划器将整个状态空间划分为重叠的局部子空间，使生成器能高效识别高奖励区域。引入启发式策略动态切换子区域，避免在已探索或低奖励区域浪费计算资源。通过迭代探索这些局部空间，生成器逐步收敛至全局高奖励子区域。实验表明，该方法在多个常用数据集上均实现更快收敛，不仅生成候选的平均奖励更高，且多样性显著提升。
原文摘要 · Abstract (English)
Generative Flow Networks (GFlowNets) have shown promising potential to generate high-scoring candidates with probability proportional to their rewards. As existing GFlowNets freely explore in state space, they encounter significant convergence challenges when scaling to large state spaces. Addressing this issue, this paper proposes to restrict the exploration of actor. A planner is introduced to partition the entire state space into overlapping partial state spaces. Given their limited size, these partial state spaces allow the actor to efficiently identify subregions with higher rewards. A heuristic strategy is introduced to switch partial regions thus preventing the actor from wasting time exploring fully explored or low-reward partial regions. By iteratively exploring these partial state spaces, the actor learns to converge towards the high-reward subregions within the entire state space. Experiments on several widely used datasets demonstrate that \modelname converges faster than existing works on large state spaces. Furthermore, \modelname not only generates candidates with higher rewards but also significantly improves their diversity.
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