Local Search GFlowNets: Enhancing GFlowNet Training with Local Search

Core Concepts

Local Search GFlowNets enhances training effectiveness by leveraging local search in object space, improving mode seeking and average reward performance.

Abstract

1. Introduction: Generative Flow Networks (GFlowNets) learn reward-proportional distributions over objects. Emphasize exploration for diverse high-reward samples. Struggle with over-exploration hindering training efficiency. 2. Main Intuition for LS-GFN: Introduces a novel algorithm, LS-GFN, combining inter-mode global exploration and intra-mode local exploration. Synergistically refines trajectories using local search to enhance training effectiveness. 3. Training Process: Step A: Sampling complete trajectories using GFlowNet policies. Step B: Refining trajectories through iterative backtracking and reconstruction guided by backward and forward policies. Step C: Training GFlowNet using revised trajectories with reward prioritized sampling. 4. Experimental Results: Significant improvement in biochemical tasks observed with LS-GFN. Outperforms prior methods in mode seeking and average reward performance.

Stats

LS-GFNは、複数の生物化学的タスクで顕著なパフォーマンス向上を実証しています。

Quotes

"LS-GFN has the fastest mode mixing capability among GFlowNet baselines and RL baselines." "Our method outperforms baselines and matches the target distribution in most cases."

Key Insights Distilled From

Local Search GFlowNets

by Minsu Kim,Ta... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2310.02710.pdf

Deeper Inquiries

How can the quality of the backward policy impact LS-GFN's performance

LS-GFN's performance can be significantly impacted by the quality of the backward policy. The backward policy plays a crucial role in the local search process by guiding the backtracking and reconstruction steps to refine candidate samples. If the backward policy is not able to effectively guide this process, it may result in suboptimal trajectories being generated during refinement. This could lead to a lower acceptance rate of refined samples, impacting the overall training effectiveness of LS-GFN.

What are the implications of deterministic filtering versus stochastic filtering in LS-GFN

Deterministic filtering and stochastic filtering in LS-GFN have different implications on the exploration and exploitation balance within the algorithm. Deterministic filtering, which accepts or rejects refined trajectories based on their rewards deterministically, tends to prioritize high-reward solutions but may limit diversity in sample generation. On the other hand, stochastic filtering introduces randomness into accepting or rejecting trajectories based on acceptance probabilities derived from Metropolis-Hastings criteria. This stochastic element promotes exploration by allowing for some low-reward solutions to be accepted, potentially leading to more diverse sample generation.

How does LS-GFN compare to other algorithms in terms of acceptance rate during training

In terms of acceptance rate during training, LS-GFN demonstrates consistent evolution between GFlowNet policies (forward and backward) and local search mechanisms throughout training iterations. The acceptance rate reflects how successful the local search is compared to GFlowNet's original sampling strategy. A steady acceptance rate indicates that there is a balanced interaction between exploring new solutions through local search and exploiting existing knowledge from GFlowNet policies during trajectory refinement processes in LS-GFN.