@inproceedings{
jang2024pessimistic,
title={Pessimistic Backward Policy for {GF}lowNets},
author={Hyosoon Jang and Yunhui Jang and Minsu Kim and Jinkyoo Park and Sungsoo Ahn},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=L8Q21Qrjmd}
}
This paper studies Generative Flow Networks (GFlowNets), which learn to sample objects proportionally to a given reward function through the trajectory of state transitions. In this work, we observe that GFlowNets tend to under-exploit the high-reward objects due to training on insufficient number of trajectories, which may lead to a large gap between the estimated flow and the (known) reward value. In response to this challenge, we propose a pessimistic backward policy for GFlowNets (PBP-GFN), which maximizes the observed flow to align closely with the true reward for the object. We extensively evaluate PBP-GFN across eight benchmarks, including hyper-grid environment, bag generation, structured set generation, molecular generation, and four RNA sequence generation tasks. In particular, PBP-GFN enhances the discovery of high-reward objects, maintains the diversity of the objects, and consistently outperforms existing methods.
Pessimistic Backward Policy for GFlowNets
Hyosoon Jang Yunhui Jang Minsu Kim Jinkyoo Park Sungsoo Ahn