Summary of Looking Backward: Retrospective Backward Synthesis For Goal-conditioned Gflownets, by Haoran He and Can Chang and Huazhe Xu and Ling Pan
Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets
by Haoran He, Can Chang, Huazhe Xu, Ling Pan
First submitted to arxiv on: 3 Jun 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 Generative Flow Networks (GFlowNets) are a family of probabilistic samplers capable of generating diverse sets of high-reward candidates. While goal-conditioned GFlowNets aim to train a single model for different outcomes, they face challenges due to sparse rewards in high-dimensional problems. To address these issues, we propose RBS (Retrospective Backward Synthesis), a novel method that synthesizes new backward trajectories in goal-conditioned GFlowNets to enrich training data and introduce learnable signals. Our approach improves sample efficiency by a large margin and outperforms strong baselines on various standard evaluation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train computers to make good decisions. Usually, these computers are only trained to find the best solution, but this method lets them generate lots of different solutions that could be good too. The problem with training these computers is that they often don’t get enough information to learn well. To fix this, we developed a new approach called RBS that helps the computer learn more effectively by providing it with extra information. Our approach works really well and outperforms other methods on many different tests. |
