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[2602.01749] Controlling Exploration-Exploitation in GFlowNets via Markov Chain Perspectives
Summary
This paper explores the relationship between Generative Flow Networks (GFlowNets) and Markov chains, introducing a framework to control exploration-exploitation dynamics, enhancing mode discovery in various benchmarks.
Why It Matters
Understanding the exploration-exploitation trade-off in GFlowNets is crucial for improving generative models in AI. This research provides a theoretical foundation and practical methods to optimize these networks, potentially leading to significant advancements in machine learning applications.
Key Takeaways
- Introduces $eta$-GFNs for better control over exploration-exploitation.
- Establishes a link between GFlowNets and Markov chain reversibility.
- Demonstrates improved mode discovery in benchmarks, outperforming previous objectives.
Computer Science > Artificial Intelligence arXiv:2602.01749 (cs) [Submitted on 2 Feb 2026 (v1), last revised 26 Feb 2026 (this version, v3)] Title:Controlling Exploration-Exploitation in GFlowNets via Markov Chain Perspectives Authors:Lin Chen, Samuel Drapeau, Fanghao Shao, Xuekai Zhu, Bo Xue, Yunchong Song, Mathieu Laurière, Zhouhan Lin View a PDF of the paper titled Controlling Exploration-Exploitation in GFlowNets via Markov Chain Perspectives, by Lin Chen and 7 other authors View PDF HTML (experimental) Abstract:Generative Flow Network (GFlowNet) objectives implicitly fix an equal mixing of forward and backward policies, potentially constraining the exploration-exploitation trade-off during training. By further exploring the link between GFlowNets and Markov chains, we establish an equivalence between GFlowNet objectives and Markov chain reversibility, thereby revealing the origin of such constraints, and provide a framework for adapting Markov chain properties to GFlowNets. Building on these theoretical findings, we propose $\alpha$-GFNs, which generalize the mixing via a tunable parameter $\alpha$. This generalization enables direct control over exploration-exploitation dynamics to enhance mode discovery capabilities, while ensuring convergence to unique flows. Across various benchmarks, including Set, Bit Sequence, and Molecule Generation, $\alpha$-GFN objectives consistently outperform previous GFlowNet objectives, achieving up to a $10 \times$ increase in the numb...
