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Jan-Philipp Fränken

Jan-Philipp Fränken has 4 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Rafael Rafailov, Alon Albalak, Chase Blagden.

4 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 6 · 26 works · 111 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 152 times in our library · ⭐ Paper Stars ranking: #7219

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Generative Reward Models (2024 · 58 citations)
    Reward models trained with the Bradley-Terry objective on human preference pairs score well on familiar data but generalize poorly to unseen tasks.
  2. Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought (2025 · 47 citations)
    Large language models handle routine math well but still fail on harder problems. The authors argue that ordinary Chain-of-Thought (CoT) is part of the cause. Written solutions in training data show only the polished final derivation. They omit the hidden, non-linear work of trying options, checking them and…
  3. STaR-GATE: Teaching Language Models to Ask Clarifying Questions (2024 · 43 citations)
    Users often phrase their requests to a language model incompletely, so the model responds generically and ignores their preferences.
  4. Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels (2024 · 16 citations)
    Teaching a language model to respect a set of behavioral rules (a "constitution") usually depends on human preference labels, curated demonstrations, or reward models with reinforcement learning.

 Co-authors here

12 of 15 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 152 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

Updated

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