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Distribution Matching Evolutionary Algorithms for Rare Event Sampling — arXiv:2610.03833

Source: arXiv
Original: https://arxiv.org/abs/2610.03833
License (as archived): CC0-1.0
Archived: 2026-10-06T05:25:51.295Z
SHA-256 of the archived text: 1792bd9b54199544ca75a488863cdfd1f66389d450104f03ebb465d984669185
Stable URL of this page: https://metraipolis.com/documento/dc_df695dbadffa

How to cite

Distribution Matching Evolutionary Algorithms for Rare Event Sampling — arXiv:2610.03833 — arXiv, https://arxiv.org/abs/2610.03833 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_df695dbadffa (Metraipolis archive dc_df695dbadffa, sha256 1792bd9b54199544ca75a488863cdfd1f66389d450104f03ebb465d984669185)

BibTeX

@misc{dcdf695dbadffa,
  title = {Distribution Matching Evolutionary Algorithms for Rare Event Sampling — arXiv:2610.03833},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.03833},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_df695dbadffa; sha256 1792bd9b54199544ca75a488863cdfd1f66389d450104f03ebb465d984669185},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.03833. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A novel discovery is one which is both useful and surprising: a generative model's output is a useful discovery if it has a low probability of being generated (it's surprising) and a high reward (it's useful). Global optimization can directly increase the probability of sampling high rewards but typically requires updating model weights. Such gradient based optimization is expensive and bars using capable closed-source models. Instead, modern search methods for discovery sacrifice the global target, and use evolutionary algorithms with local reward maximizing objectives, permitting the search to focus only on high probability samples. In this paper, we interpret various evolutionary algorithms as approximate Markov Chain Monte Carlo, an optimization-free method to sample from complex distributions. This interpretation allows developing Distribution Matching Evolutionary Algorithms (DME), a class of search methods which sample from a global target distribution without updating weights. Empirically, DME has a higher sample efficiency than existing methods on problems requiring many samples to find a solution.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.03833 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:05.750Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).