CoRe: Combined Rewards with Vision-Language Model Feedback for Preference-Aligned Reinforcement Learning

• URL: http://arxiv.org/abs/2607.01721v1
• Date: Thu, 02 Jul 2026 05:20:47 GMT
• Title: CoRe: Combined Rewards with Vision-Language Model Feedback for Preference-Aligned Reinforcement Learning
• Abstract summary: CoRe is a framework that integrates Formal Rewards (FR) and Residual Rewards (RR) with vision-language models (VLMs) feedback to achieve preference-aligned policies.<n>Our contributions are twofold: (1) We propose a Formal Reward Module (FRM) that leverages VLMs to iteratively design FR based on task knowledge and preference feedback, enabling the continual improvement of policy during training; (2) We introduce a Residual Reward Module (RRM) that learns RR from video-level preference by employing VLMs to generate preference labels and capture nuanced rewards that complement FR, ensuring alignment with human intent.
• Score: 6.874780979291594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reward design remains a central challenge in reinforcement learning (RL). Hand-crafted rewards are often difficult to specify and may lead to suboptimal policies, while learned rewards from preferences can suffer from inefficiency and unstable training. Inspired by the dual nature of human learning explored in cognitive science, we decompose rewards into two complementary components: Formal Rewards (FR), explicitly designed based on task knowledge, and Residual Rewards (RR), learned from observations to capture implicit and nuanced preferences. Based on this decomposition, we propose CoRe, a hybrid framework that integrates FR and RR with vision-language models (VLMs) feedback to achieve preference-aligned policies without human involvement. Our contributions are twofold: (1) We propose a Formal Reward Module (FRM) that leverages VLMs to iteratively design and optimize FR based on task knowledge and preference feedback, enabling the continual improvement of policy during training; (2) We introduce a Residual Reward Module (RRM) that learns RR from video-level preference by employing VLMs to generate preference labels and capturing nuanced rewards that complement FR, ensuring alignment with human intent. Through the synergy of FRM and RRM, CoRe enables the automatic construction of reliable rewards that are efficient and preference-aligned. Extensive experiments demonstrate that CoRe outperforms existing approaches in terms of policy learning effectiveness and efficiency on ten robotic manipulation tasks in simulation and five real-worlds. Videos can be found on our project website: https://core-2026.github.io/

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