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Marianne Arriola

Marianne Arriola has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Volodymyr Kuleshov, Yair Schiff, Alexander M. Rush.

2 papers in our library · 2024–2025

OpenAlex · h-index 1 · 11 works · 44 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 111 times in our library · ⭐ Paper Stars ranking: #7343

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Cornell UniversityIthaca · United States2024–2026 · 2 paperscurrent🎓Cornell TechNew York · United States2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Simple and Effective Masked Diffusion Language Models (2024 · 152 citations)
    Diffusion methods have trailed autoregressive (AR) models in language modeling likelihood. This work argues the gap is smaller than earlier reports suggested. The authors restrict themselves to masking (absorbing-state) discrete diffusion and present MDLM, which pairs careful modern training practice with a cleaner…
  2. d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation (2025 · 4 citations)
    Reinforcement learning is the standard way to add reasoning skills to autoregressive LLMs. It is harder to apply to masked diffusion language models (DLMs) because policy-gradient updates need the likelihood of the whole sampling trajectory. Computing that likelihood naively takes one forward pass per denoising step.…

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 111 times (of the 149,439 papers whose bibliographies we read); 29 citing papers with a page here →.

Updated

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