1 citations · 1 across the 5 of their papers we have counts for
7 papers
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Riccardo De Santi, Bruce Lee, Cristian Perez Jensen +6
Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In genera…
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Zifan Wang, Riccardo De Santi, Xiaoyu Mo +3
Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment. Existing entropy-regularized methods primarily maximize expe…
Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning
Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh +3
Adapting large-scale foundation flow and diffusion generative models to optimize task-specific objectives while preserving prior information is crucial for real-world applications…
Optimistic Task Inference for Behavior Foundation Models
Thomas Rupf, Marco Bagatella, Marin Vlastelica +1
Behavior Foundation Models (BFMs) are capable of retrieving high-performing policy for any reward function specified directly at test-time, commonly referred to as zero-shot reinfo…
Provable Maximum Entropy Manifold Exploration via Diffusion Models
Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh +3
Exploration is critical for solving real-world decision-making problems such as scientific discovery, where the objective is to generate truly novel designs rather than mimic exist…
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Jiawei Huang, Niao He, Andreas Krause
We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash…