Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
Daniel Yang, Samuel Stante, Florian Redhardt +5
Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that overlook the epistemic uncert…
cs.LG2025
Scaling can lead to compositional generalization
Florian Redhardt, Yassir Akram, Simon Schug
Can neural networks systematically capture discrete, compositional task structure despite their continuous, distributed nature? The impressive capabilities of large-scale neural ne…
cs.LG2024
When can transformers compositionally generalize in-context?
Seijin Kobayashi, Simon Schug, Yassir Akram +5
Many tasks can be composed from a few independent components. This gives rise to a combinatorial explosion of possible tasks, only some of which might be encountered during trainin…