--- title: "Jonas Hubotter · ETH Zurich — author profile and papers" description: "Jonas Hubotter, ETH Zurich: 2 papers on Elamur Research (2026), h-index 2 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/jonas-hubotter/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jonas-hubotter/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Jonas Hubotter # Jonas Hubotter Jonas Hubotter has 2 papers in our library, in 2026, most of them on [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Idan Shenfeld](https://md.research.elamur.ai/free/en/authors/idan-shenfeld/), [Carlos Guestrin](https://md.research.elamur.ai/free/en/authors/carlos-guestrin/), [Ido Hakimi](https://md.research.elamur.ai/free/en/authors/ido-hakimi/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5082779019) · [**h-index 2**](https://openalex.org/authors/A5082779019) · [29 works](https://openalex.org/works?filter=author.id:A5082779019) · [11 citations](https://openalex.org/works?filter=author.id:A5082779019&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/jonas-hubotter/#papers) · [🔗 Cited 136 times in our library](https://md.research.elamur.ai/free/en/authors/jonas-hubotter/cited-by/) · [⭐ Paper Stars ranking: #7693](https://md.research.elamur.ai/free/en/paper-stars/page/77/#star-7693) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Jonas+Hubotter) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Jonas+Hubotter) · [dblp](https://dblp.org/search?q=Jonas+Hubotter) · [ACL Anthology](https://aclanthology.org/search/?q=Jonas+Hubotter) ## Affiliations [🎓**ETH Zurich** Zurich · Switzerland 2025–2026 · 8 papers*current*](https://md.research.elamur.ai/free/en/universities/eth-zurich/) 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 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 1 Research subjects and languages the work is about.[Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/jonas-hubotter/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Reinforcement Learning via Self-Distillation](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-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](https://md.research.elamur.ai/free/en/papers/shenfeld-2026-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. - [Idan Shenfeld](https://md.research.elamur.ai/free/en/authors/idan-shenfeld/) ×2 - [Carlos Guestrin](https://md.research.elamur.ai/free/en/authors/carlos-guestrin/) - [Ido Hakimi](https://md.research.elamur.ai/free/en/authors/ido-hakimi/) - [Mehul Damani](https://md.research.elamur.ai/free/en/authors/mehul-damani/) ## 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 →](https://md.research.elamur.ai/free/en/authors/jonas-hubotter/cited-by/). Updated 3 November 2026