MPB ench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification (2025)

Abstract: Reasoning is an essential capacity for large language models (LLMs) to address complex tasks, whereas the identification of process errors is vital for improving this ability. Recently, process-level reward models (PRMs) were proposed to provide step-wise rewards that facilitate reinforcement learning and data production during training and guide LLMs toward correct steps during inference, thereby improving reasoning accuracy. However, existing benchmarks of PRMs are text-based and focus on error detection, neglecting other scenarios like reasoning search. To address this gap, we introduce MPBench, a comprehensive, multi-task, multimodal benchmark designed to systematically assess the effectiveness of PRMs in diverse scenarios. MPBench employs three evaluation paradigms, each targeting a specific role of PRMs in the reasoning process: (1) Step Correctness, which assesses the correctness of each intermediate reasoning step; (2) Answers Aggregation, which aggregates multiple solutions and selects the best one; and (3) Reasoning Process Search, which guides the search for optimal reasoning steps during inference. Through these paradigms, MPBench makes comprehensive evaluations and provides insights into the development of multimodal PRMs.

## introduction artificial intelligence @xcite has not yet effectively addressed complex reasoning tasks such as mathematics, programming, and planning. a barrier to achieving artificial general intelligence (agi) remains this. to address this challenge, the recent release of the gpt-o1 (openai, 2024) proposed learning to reason to master human-like reasoning processes. unlike traditional llms' thinking, reasoning models generate long chains of thought. consequently, recent research has increasingly paid attention to process-level (i.e., stepwise) reasoning analysis and further introduced various reasoning models to achieve significant enhancements in tasks such as mathematics and code generation. identifying process errors can facilitate effective reasoning trajectories. prms, introduced by @xcite and @xcite , can provide stepwise feedback for multi-step reasoning results. by rewarding intermediate steps, prms can enhance llms' reasoning capabilities during both training and inference. specifically, prms facilitate reinforcement learning or chain-of-thought data generation by offering fine-grained rewards, thus reducing the dependence on human-annotated data and improving performance. moreover, during inference, prms guide llms in evaluating and exploring intermediate "thoughts," promoting the generation of more deliberate reasoning steps and ultimately leading to improved reasoning accuracy. despite the critical role of prms, they have not been adequately evaluated. best-of-n performance, a widely employed evaluation paradigm, is time-consuming, lacks finer-grained inspection, and its evaluation reliability can be significantly affected by the underlying solution generation model. this limited scope of evaluation hinders a comprehensive understanding of prms' potential to enhance complex reasoning tasks. recent benchmarks @xcite @xcite were introduced to evaluate prms. however, they typically focus on a single scenario, such as step-by-step error identification, evaluating the ability of prms to detect erroneous steps accurately. these evaluation paradigms inadequately assess prm performance in some scenarios. for example, during the reasoning process search in llm inference, prms are tasked with selecting the correct step from candidates without access to the complete reasoning trajectory and the final answer. furthermore, whereas current prm benchmarks are text-centric, multimodal benchmarks are deficient, while multimodal contents are common in real-world tasks. these challenges motivate the development of mpbench, a comprehensive multimodal benchmark designed to evaluate the efficacy of multimodal process-level reward models (prms) across three distinct scenarios. mpbench centers on three key evaluation paradigms: step correctness, which assesses prms' ability to evaluate the correctness of each intermediate reasoning step and provide stepwise rewards to support reinforcement learning; answer aggregation, where prms aggregate per-step scores from multiple candidate solutions to select the optimal one; and reasoning process search, which examines prms' capacity to guide the search for optimal reasoning steps by enabling a structured exploration of potential solutions. @xcite fine-grained data instances across these three scenarios, mpbench offers a robust framework for comprehensively assessing prm performance in real-world reasoning tasks, thereby providing valuable insights into their role in enhancing the reasoning capabilities of mllms. we conduct extensive experiments on mpbench with 14 mllms (prompted as critic models), including closed-source models, gpt-4o, claude-3.7 and gemini-2.0, and open-source models like internvl @xcite , qwenvl @xcite , qvq @xcite , and visualprm @xcite . they struggle to achieve satisfactory results, and we believe our benchmarks benefit the future development of multimodal prms and process-level analysis. our key contributions are summarized as follows: these reasoning benchmarks are primarily textbased. with the increasing demand for evaluating multimodal reasoning, there has been a drive to develop multimodal reasoning benchmarks across diverse domains @xcite . however, research on benchmarks specifically tailored to multimodal prms remains limited. to address this gap, we introduce mpbench to benchmark multimodal process reward models. as highlighted in ## mpbench mpbench is a comprehensive benchmark designed to evaluate how process-level reward models (prms) enhance reasoning, comprising 9,745 finegrained instances. it employs three core evaluation paradigms: step correctness, which assesses prms' ability to judge the correctness of individual intermediate reasoning steps, providing stepwise rewards for reinforcement learning @xcite and reducing reliance on human-annotated data; answer aggregation, which tasks prms with aggregating per-step scores from multiple candidate solutions to identify the best one; and reasoning process search, which evaluates prms' capacity to guide the search for optimal reasoning steps during inference by enabling a tree search over the solution space, thereby encouraging more deliberate reasoning and improving accuracy. ## experiments to provide a comprehensive evaluation of various models on mpbench, we selected a diverse set, encompassing both open-source and proprietary llms. this selection includes advanced models such as gpt-4 and multi-step reasoningenhanced llms like the gemini-2-thinking (deep-mind, 2024). given the complexity of the tasks, few-shot demonstration setups were employed to facilitate model adaptation to the required output format through in-context learning (icl). we utilized two-shot examples when prompting generalpurpose llms. the impact of varying few-shot settings is discussed in section 4.1.4. the prompts for the above phases are detailed in appendix e. ## conclusions this work presents mpbench, a novel multimodal benchmark designed for evaluating error identification in reasoning processes. through a detailed analysis of mllm performance on mpbench, we have shed light on the strengths and weaknesses of these models when used as criteria for enhancing reasoning. specifically, we examined three key aspects: step correctness identification, answer aggregation, and reasoning process search. our investigation revealed a linear correlation coupled with subtle inconsistencies between them. additionally, our analysis across diverse domains (science, commonsense, and mathematics) demonstrated that mathematical reasoning presents a significant challenge for current mllms, with performance lagging behind that observed in other domains. this suggests that future research should prioritize the development of prms and training methodologies that are more robust to the complexities of mathematical reasoning.