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Shiv Shankar

Shiv Shankar has 3 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning.

3 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 3 · 45 works · 29 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11402

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

🎓University of Massachusetts AmherstAmherst Center · United States2019–2026 · 14 paperscurrent🎓Vivekananda Global UniversityJaipur · India2024 · 25 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

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

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Energy Matching based Preference Learning for Diffusion Language Models (2026 · 1 citation)
    Policy-gradient RL methods such as GRPO depend on sequence likelihoods that factorize token by token. Diffusion language models (dLLMs) generate text non-autoregressively, so these likelihoods are intractable. ELBO-based approximations introduce bias, and off-policy importance weights become unstable over long…
  2. Learning Shortcut Models for Efficient Recursive Reasoning (2026)
    Recursive latent reasoners such as HRM and TRM refine hidden states through many small sequential passes. Their inference cannot be parallelised, they receive weak supervision for early steps, and they cannot adjust their compute budget at test time. This paper views the iterative refinement steps of models in the…
  3. Pseudo-Likelihood Training for Reasoning Diffusion Language Models (2026)
    Policy-gradient RL methods such as PPO and GRPO are the standard way to post-train LLMs for reasoning. They need token-level likelihoods of sampled outputs, which masked diffusion language models (dLLMs) cannot provide cheaply. Existing workarounds either use expensive Monte-Carlo ELBO estimates or a one-step…

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 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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