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.
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
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Fields, topics and concepts
📚 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 worksInformation Systems · 1 work
🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work
Works by year
2020s
Papers
- 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… - 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…
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