Stable GFlowNets with Probabilistic Guarantees
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915975725580288 |
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| author | Lei, Zengxiang Shreekumar, Ananth Rosenthal, Jonathan Song, Ruoyu Cardenas, Alvaro A. Fremont, Daniel J. Xu, Dongyan Ukkusuri, Satish Celik, Z. Berkay |
| author_facet | Lei, Zengxiang Shreekumar, Ananth Rosenthal, Jonathan Song, Ruoyu Cardenas, Alvaro A. Fremont, Daniel J. Xu, Dongyan Ukkusuri, Satish Celik, Z. Berkay |
| contents | Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward. Despite their theoretical promise, practical training is often unstable, exhibiting severe loss spikes and mode collapse. To tackle this, we first assess the sensitivity of GFlowNet objectives, demonstrating that a small Total Variation (TV) distance between the learned and target distributions does not preclude unbounded training loss. Motivated by this mismatch, we establish converse guarantees by deriving loss-to-TV bounds that certify global fidelity from bounded trajectory balance losses. Lastly, we propose Stable GFlowNets, an algorithm that leverages our theoretical results to stabilize training, and empirically demonstrate improved training behavior and superior distributional fidelity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01729 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stable GFlowNets with Probabilistic Guarantees Lei, Zengxiang Shreekumar, Ananth Rosenthal, Jonathan Song, Ruoyu Cardenas, Alvaro A. Fremont, Daniel J. Xu, Dongyan Ukkusuri, Satish Celik, Z. Berkay Machine Learning Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward. Despite their theoretical promise, practical training is often unstable, exhibiting severe loss spikes and mode collapse. To tackle this, we first assess the sensitivity of GFlowNet objectives, demonstrating that a small Total Variation (TV) distance between the learned and target distributions does not preclude unbounded training loss. Motivated by this mismatch, we establish converse guarantees by deriving loss-to-TV bounds that certify global fidelity from bounded trajectory balance losses. Lastly, we propose Stable GFlowNets, an algorithm that leverages our theoretical results to stabilize training, and empirically demonstrate improved training behavior and superior distributional fidelity. |
| title | Stable GFlowNets with Probabilistic Guarantees |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.01729 |