Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
arXiv:2604.22948v2 Announce Type: replace Abstract: History-dependent sampling can reduce long-run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history through empirical measure on finite state spaces, which is infeasible in high-dimensional discrete configuration spaces or ill-posed in continuous domains. We propose Score-Repellent Monte Carlo (SRMC) framework that summarizes trajectory history by a running average of score evaluations in $\mathbb{R}^d$, where $d$ is the dimension of the score and state representation. This history is converte
The continuous drive for more efficient and robust sampling methods in complex AI models, especially those involving high-dimensional spaces, is a persistent and evolving research frontier.
Improved Monte Carlo methods can significantly enhance the training and inference capabilities of AI systems, leading to more accurate models and enabling applications previously hindered by computational limitations.
This research introduces a novel way to handle history-dependent sampling, potentially reducing variance and making previously intractable problems in high-dimensional spaces more feasible, which could impact various AI applications.
- · AI researchers
- · Machine learning engineers
- · Generative AI companies
- · Simulation developers
- · Inefficient sampling methods
- · Models reliant on high computational overhead for sampling
More efficient and accurate sampling leads to faster development and deployment of advanced AI models.
This could enable new classes of AI applications in areas like drug discovery, material science, or complex system modeling.
The reduced computational cost for certain AI tasks may accelerate general AI development and accessibility.
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Read at arXiv cs.LG