Subspace Inference Enables Efficient Active Reward Learning from Preferences

arXiv:2609.04066 · cs.LG, cs.AI, cs.RO · Submitted 2026-09-03 · Read on arXiv

cs.LG, cs.AI, cs.RO

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/yutaizhou/bnn_pref

Terminology

• Acquisition Function — 39× in this paper · explained in 1 episode
• Reinforcement Learning — 24× in this paper · explained in 4 episodes
• Active Learning — 23× in this paper · explained in 6 episodes
• Calibration — 21× in this paper · explained in 7 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Sample Efficiency — 10× in this paper · explained in 4 episodes
• Adam — 9× in this paper · explained in 2 episodes
• Uncertainty Quantification — 9× in this paper · explained in 8 episodes
• Reward Function — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Bayesian Inference — 5× in this paper · explained in 5 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Q-learning — 5× in this paper · explained in 3 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Implicit Q-Learning — 4× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Initial Data — 2× in this paper · explained in 2 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Intrinsic Dimension — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

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