Bayesian Preference Learning for Test-Time Steerable Reward Models

arXiv:2602.08819 · cs.LG, cs.CL · Submitted 2026-02-09 · Read on arXiv

cs.LG, cs.CL

Submitted: 2026-02-09

Updated: 2026-10-19

Terminology

• Verifiable Rewards — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Bradley-Terry model — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Variational In-Context Reward Modeling — 1× in this paper

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks