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Raphaël Baur

2 accepted papers

2026

Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference

ICML 2026poster

Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as demonstrations, comparisons, ratings, rankings, and stops t…

Cited by 0SourceScholar
2025

Reward Learning from Multiple Feedback Types

ICLR 2025poster

Learning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison between multiple completions, is an established method to acquire large-scale human feedback. However, human feedback in othe…