Reward-Conditioned Reinforcement Learning

Michal Nauman; Marek Cygan; Pieter Abbeel

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arXiv · 2603.05066

Reward-Conditioned Reinforcement Learning

Michal Nauman·Marek Cygan·Pieter Abbeel

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Abstract

Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their ability to adapt to changing preferences. We introduce Reward-Conditioned Reinforcement Learning (RCRL), an off-policy method that conditions agents on reward parameterizations while collecting experience under a single nominal objective. By recomputing counterfactual rewards from shared replay data, RCRL exposes the agent to multiple reward objectives without additional environment interaction, connecting single-task RL with ideas from multi-objective and multi-task learning. Across single-task, multi-task, and vision-based benchmarks, RCRL improves sample efficiency under the nominal reward parameterization, enables efficient adaptation to new parameterizations, and supports zero-shot behavioral adjustment at deployment. Our results show that RCRL provides a scalable mechanism for learning robust, steerable policies without sacrificing the simplicity of single-task training.

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Michal Nauman, Marek Cygan, Pieter Abbeel. 2026-05-19. Reward-Conditioned Reinforcement Learning. https://arxiv.org/abs/2603.05066

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