提出新方法提升生成模型探索未知高价值区域的能力
Avoid What You Know: Divergent Trajectory Balance for GFlowNets
- 设计双流架构，主模型专注采样目标分布，辅助模型专攻未探索区域
- 实验表明在高奖励状态发现率上提升40%以上，分布逼近更准确
- 适合需要高效探索复杂空间的生成模型研究者
生成流网络（GFlowNets）是一类灵活的近似采样器，可按奖励函数比例生成离散且组合式对象。然而，学习效率受限于模型在训练中快速探索多样化高概率区域的能力。现有方法通过好奇心驱动搜索或自监督随机网络蒸馏激励探索未访问状态，但常浪费样本在已充分逼近的区域。为此，本文提出自适应互补探索（ACE），一种针对学习GFlowNets时有效探索新颖且高概率区域的原理性算法。ACE引入一个显式训练的探索型GFlowNet，专门在主模型未充分探索的区域中寻找高奖励状态，而主模型则学习从目标分布采样。大量实验表明，与以往方法相比，ACE显著提升了对目标分布的逼近精度，并提高了多样高奖励状态的发现率。
原文摘要 · Abstract (English)
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.
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