Summary of Pessimistic Backward Policy For Gflownets, by Hyosoon Jang et al.
Pessimistic Backward Policy for GFlowNets
by Hyosoon Jang, Yunhui Jang, Minsu Kim, Jinkyoo Park, Sungsoo Ahn
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates Generative Flow Networks (GFlowNets) that learn to sample objects based on a given reward function through state transitions. The study finds that GFlowNets tend to under-exploit high-reward objects due to insufficient training data, leading to a gap between estimated flow and true reward values. To address this challenge, the authors propose a pessimistic backward policy for GFlowNets (PBP-GFN), which maximizes observed flow to align with the true reward. The approach is evaluated across eight benchmarks, including hyper-grid environment, bag generation, structured set generation, molecular generation, and RNA sequence generation tasks. Results show that PBP-GFN enhances high-reward object discovery, maintains diversity, and outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a special type of computer program called Generative Flow Networks (GFlowNets). These programs try to find objects that are good or bad based on how well they do in certain situations. The problem is that these programs don’t always find the best objects because they don’t have enough information. To fix this, the researchers created a new way of using GFlowNets called PBP-GFN. They tested it on many different types of tasks and found that it did a better job than other methods. |
