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MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
Raphaël Baur, Yannick Metz, Maria Gkoulta +3
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 lear…
Reward Learning from Multiple Feedback Types
Yannick Metz, András Geiszl, Raphaël Baur +1
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 betwe…
Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework
Yannick Metz, David Lindner, Raphaël Baur +1
Reinforcement Learning from Human feedback (RLHF) has become a powerful tool to fine-tune or train agentic machine learning models. Similar to how humans interact in social context…
RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback
Yannick Metz, David Lindner, Raphaël Baur +2
To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to learn reward models from diverse sources of human feedback and to consider huma…