4 citations · 5 across the 12 of their papers we have counts for
6 papers · 2 filters
Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning
Jonas Hübotter, Leander Diaz-Bone, Ido Hakimi +2
Humans are good at learning on the job: We learn how to solve the tasks we face as we go along. Can a model do the same? We propose an agent that assembles a task-specific curricul…
Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
Jonas Hübotter, Patrik Wolf, Alexander Shevchenko +3
Recent empirical studies have explored the idea of continuing to train a model at test-time for a given task, known as test-time training (TTT), and have found it to yield signific…
Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning
Matthias Otth, Jonas Hübotter, Ido Hakimi +1
Recent work has shown that language models can self-improve by maximizing their own confidence in their predictions, without relying on external verifiers or reward signals. In thi…
Test-time Offline Reinforcement Learning on Goal-related Experience
Marco Bagatella, Mert Albaba, Jonas Hübotter +2
Foundation models compress a large amount of information in a single, large neural network, which can then be queried for individual tasks. There are strong parallels between this…
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter +1
Sparse-reward reinforcement learning (RL) can model a wide range of highly complex tasks. Solving sparse-reward tasks is RL's core premise, requiring efficient exploration coupled…
Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging
Ryo Bertolissi, Jonas Hübotter, Ido Hakimi +1
Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language mo…