Life sciences · Preprint

Subspace Inference Enables Efficient Active Reward Learning from Preferences

arXiv · September 3, 2026

Preprint: Not Peer Reviewed Why this matters to you Expert discussion

Posted before peer review. The findings may change or fail to hold.

AI Summary

This is an unpublished preprint describing PreferenceEKF, a computational method for efficient active learning of reward models from human preferences using subspace Bayesian filtering. The authors report improved sample efficiency, runtime, and scalability on standard reinforcement learning benchmarks compared to other Bayesian deep learning approaches, but the work is algorithmic and has not been peer-reviewed or validated in human-in-the-loop settings.

What was studied

Preprint. Intervention: PreferenceEKF: sequential Bayesian filtering of reward model parameters in a low-dimensional subspace, with active preference query acquisition. Compared with: Other Bayesian deep learning approaches for uncertainty quantification in reward learning.

Key findings

PreferenceEKF achieves better sample efficiency and runtime scalability than other Bayesian deep learning approaches on D4RL and V-D4RL benchmarks Learned reward models lead to competitive offline reinforcement learning policy performance Method demonstrates improved calibration compared to baseline Bayesian approaches

Safety

Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.

Who this may help

The source did not state who this applies to in practice.

Plain-language summary

This is an unrefereed arXiv preprint presenting a novel computational method for reward model training, not a clinical trial or peer-reviewed evidence synthesis; it reports algorithmic improvements but has not undergone peer review.

Study design

As stated by the source record.

InterventionPreferenceEKF: sequential Bayesian filtering of reward model parameters in a low-dimensional subspace, with active preference query acquisition

ComparatorOther Bayesian deep learning approaches for uncertainty quantification in reward learning

Caveats

Preprint has not undergone peer review
No numerical results (sample sizes, effect sizes, p-values, confidence intervals) are reported in the source text
Validation is limited to computational benchmarks (D4RL, V-D4RL); no human preference data or human-in-the-loop evaluation reported
Generalizability to real-world RLHF systems or clinical domains is not established

Evidence assessment

Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.

Study design

insufficient

Preprint method paper; no clinical or empirical human study design reported.

Directness

limited

Benchmarked on D4RL and V-D4RL datasets; no direct human preference or clinical endpoint data.

Consistency

insufficient

Single method proposal; no systematic comparison with other uncertainty quantification approaches detailed.

Risk of bias

insufficient

Preprint; no peer review, blinding, or registration. Computational experiments only.

Sample adequacy

insufficient

Benchmarks on standard RL datasets; sample size and power not applicable to method development.

What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.

Source 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 sequential Bayesian filtering problem. Instead of relying on computationally prohibitive posterior inference over the full neural network parameter space, our method performs sequential inference via an extended Kalman filter within a low-dimensional parameter subspace, continuously updating the reward model posterior as new preference queries arrive. Our approach enables scalable sampling of neural network parameters to efficiently compute acquisition functions for active reward learning. Experiments on the D4RL and V-D4RL benchmarks demonstrate that our approach achieves better sample efficiency, runtime, scalability, and calibration compared to other Bayesian deep learning approaches, and the learned reward models lead to competitive offline reinforcement learning policy performance. This highlights the potential of scalable Bayesian methods for preference-based reward modeling in RLHF. Our code is available at https://github.com/yutaizhou/bnn_pref.

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Erdem BıyıkYutai Zhou