Perception-R1: Advancing Multimodal Reasoning Capabilities of MLLMs via Visual Perception Reward

ICLR 2026 Conference Submission2898 Authors

Keywords: Multimodal Large Language Models, Multimodal Reasoning, Reinforcement Learning

TL;DR: We observe that standard RLVR fails to enhance the MLLMs perception. Therefore, we propose a novel visual perception reward to improve the MLLMs perception in RLVR, effectively boosting performance on several multimodal benchmarks with limited data.

Abstract: Enhancing the multimodal reasoning capabilities of Multimodal Large Language Models (MLLMs) is a challenging task that has attracted increasing attention in the community. Recently, several studies have applied Reinforcement Learning with Verifiable Rewards (RLVR) to the multimodal domain in order to enhance the reasoning abilities of MLLMs. However, these works largely overlook the enhancement of multimodal perception capabilities in MLLMs, which serve as a core prerequisite and foundational component of complex multimodal reasoning. Through McNemar's test, we find that existing RLVR method fails to effectively enhance the multimodal perception capabilities of MLLMs, thereby limiting their further improvement in multimodal reasoning. To address this limitation, we propose Perception-R1, which introduces a novel visual perception reward that explicitly encourages MLLMs to perceive the visual content accurately, thereby can effectively incentivizing both their multimodal perception and reasoning capabilities. Specifically, we first collect textual visual annotations from the CoT trajectories of multimodal problems, which will serve as visual references for reward assignment. During RLVR training, we employ a judging LLM to assess the consistency between the visual annotations and the responses generated by MLLM, and assign the visual perception reward based on these consistency judgments. Extensive experiments on several multimodal math and general benchmarks demonstrate the effectiveness and robustness of our Perception-R1, which achieves superior performance on all benchmarks using only 1,442 training data.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 2898