← Back to all news
arXiv AI By Yutai Zhou, Erdem B{\i}y{\i}k

Subspace Inference Enables Efficient Active Reward Learning from Preferences

Read the original on arXiv AI →

The paper introduces PreferenceEKF, a sample‑efficient method for active reward learning from human preferences. By framing preference learning as a sequential Bayesian filtering problem, it tracks reward model uncertainty using an extended Kalman filter in a low‑dimensional subspace, avoiding costly posterior inference over the full neural network. Experiments on D4RL and V‑D4RL benchmarks show improved sample efficiency, runtime, scalability, and calibration, with reward models that support competitive offline reinforcement learning policies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Aug 28

Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics

The paper introduces Residual Reward Models (RRM) to enhance preference‑based reinforcement learning (PbRL) in robotics. RRMs decompose the true reward into a prior component—such as a heuristic, language‑generated, or IRL‑derived reward—and a learned residual that is trained with human preferences. Experiments on Meta‑World, DM‑Control, and a physical Franka Panda robot show that RRMs markedly improve sample efficiency and accelerate policy learning compared to standard PbRL methods.

By Chenyang Cao, Miguel Rogel-Garc\'ia, Mohamed Nabail, Xueqian Wang, Nicholas Rhinehart
reinforcement-learningroboticsfine-tuning
More like this →