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#machine learning Preprint Jun 2025

Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning

A novel unified operator is introduced that combines several regularized RL operators into a general framework that better targets peakier sampling distributions and is named trajectory general mellowmax (TGM), which is shown to identify higher quality, diverse candidates than baselines in both synthetic and real-world tasks.

Marco Jiralerspong, Esther Derman, Danilo Vucetic et al. · 2 citations
#machine learning Open access Oct 2023

Expected flow networks in stochastic environments and two-player zero-sum games

This work shows that EFlowNets outperform other GFlowNet formulations in stochastic tasks such as protein design and extends the concept of EflowNets to adversarial environments, proposing adversarial flow networks (A FlowNets) for two-player zero-sum games.

Marco Jiralerspong, Bilun Sun, Danilo Vucetic et al. · 11 citations · ⚡1

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