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Jonas Hubotter

Jonas Hubotter has 2 papers in our library, in 2026, most of them on Efficient models & compression; often with Idan Shenfeld, Carlos Guestrin, Ido Hakimi.

2 papers in our library · 2026

OpenAlex · h-index 2 · 29 works · 11 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 136 times in our library · ⭐ Paper Stars ranking: #7691

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. Reinforcement Learning via Self-Distillation (2026 · 74 citations)
    Policy-gradient RL with verifiable rewards, such as GRPO, gets only one scalar score per attempt. That makes it hard to tell which tokens caused a failure, and learning stops when every rollout in a group gets the same score. The authors point out that many environments also return text explaining the outcome, such…
  2. Self-Distillation Enables Continual Learning (2026 · 62 citations)
    Foundation models usually stop learning after deployment. Fine-tuning them on new data with supervised fine-tuning (SFT) tends to erase earlier abilities, because SFT trains on a fixed external data distribution, which makes it off-policy. On-policy reinforcement learning forgets less, but it requires a reward…

 Co-authors here

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

 Cited by

Papers of our library cite their works 136 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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