UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning

Researchers introduce UBP2, a model-based reinforcement learning method that improves sample efficiency in preference-based learning by actively directing exploration through uncertainty quantification across reward, dynamics, and value functions. The approach achieves sublinear regret guarantees and demonstrates substantially higher sample efficiency than existing methods on benchmark tasks.

UBP2 addresses a fundamental challenge in preference-based reinforcement learning: the inefficiency of passive data collection during reward model learning from pairwise comparisons. Traditional approaches require extensive trial-and-error without strategically identifying which comparisons provide the most informative feedback. This research introduces active exploration by jointly reasoning over three distinct sources of uncertainty—reward model confidence, environment dynamics predictions, and value function estimates—creating a unified planning objective that naturally balances exploitation and exploration.

The method builds on growing recognition within the ML community that uncertainty quantification enables more efficient learning. By using ensembles across reward, dynamics, and value models, UBP2 identifies trajectories that reduce uncertainty most effectively rather than relying on ad hoc exploration bonuses. The theoretical contribution—establishing sublinear regret bounds for both finite and infinite horizon problems—provides formal validation that the uncertainty-balancing approach doesn't sacrifice convergence guarantees.

For developers and researchers in reinforcement learning, this work has direct implications for systems requiring human feedback or preference annotations, such as dialogue systems, robotic manipulation, and recommendation engines. Improved sample efficiency reduces the annotation burden and computational cost of training preference-based reward models, making practical deployment more feasible. The Meta-World benchmark results demonstrate tangible performance gains over both model-free baselines and non-optimistic model-based approaches, suggesting the method generalizes across diverse manipulation tasks.

Looking forward, the integration of uncertainty-driven exploration in preference-based settings could accelerate development of AI systems that learn from human feedback at scale, though the approach's applicability to more complex domains and its computational overhead during planning warrant further investigation.

• →UBP2 improves sample efficiency in preference-based RL by actively exploring through joint uncertainty reasoning across reward, dynamics, and value functions.
• →The method provides theoretical sublinear regret guarantees while avoiding ad hoc exploration heuristics through unified planning objectives.
• →Ensemble-based uncertainty quantification naturally creates exploration-exploitation tradeoffs without requiring hyperparameter tuning of bonus terms.
• →Meta-World experiments show substantially higher sample efficiency compared to model-free preference-based methods and non-optimistic baselines.
• →The approach reduces annotation burden for systems learning from human feedback, with implications for robotics, dialogue, and recommendation tasks.