arXiv Artificial Intelligence

Subspace Inference Enables Efficient Active Reward Learning from Preferences

6 days ago 04.09.2026 07:00 👁 1 views
Subspace Inference Enables Efficient Active Reward Learning from Preferences

Quick summary

arXiv:2609.04066v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learning a critical component in synthesizing informative preference queries. However, effective uncertainty quantification required for active learning remains a key challenge for large neural network reward models. In this paper, we introduce PreferenceEKF, a sample-efficient approach that tracks reward model uncertainty by framing active preference learning as a sequentia

Key takeaways

  • arXiv:2609.04066v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learning a critical component in synthesizing informative preference queries.
  • However, effective uncertainty quantification required for active learning remains a key challenge for large neural network reward models.
  • In this paper, we introduce PreferenceEKF, a sample-efficient approach that tracks reward model uncertainty by framing active preference learning as a sequentia

Why it matters

“Subspace Inference Enables Efficient Active Reward Learning from Preferences” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗