Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation

TL;DR

This paper introduces a dynamic evaluation framework for multimodal large language models that assesses their true generalization ability by perturbing tasks rather than inputs, revealing overfitting and data contamination issues.

Contribution

We propose a novel task perturbation method for evaluating MLLMs, providing deeper insights into their generalization beyond static benchmarks.

Findings

Fine-tuning on contaminated data improves task-specific performance

Models overfit to single tasks and falter under task shifts

Dynamic evaluation reveals overfitting and data leakage issues

Abstract

Multimodal Large Language Models (MLLMs) show impressive vision-language benchmark performance, yet growing concerns about data contamination (test set exposure during training) risk masking true generalization. This concern extends to reasoning MLLMs, often fine-tuned via reinforcement learning from potentially contaminated base models. We propose a novel dynamic evaluation framework to rigorously assess MLLM generalization, moving beyond static benchmarks. Instead of perturbing inputs, we perturb the task itself. Using the same visual input, models are evaluated across a family of tasks (e.g., QA, captioning, question posing, verification) to probe diverse capabilities. This task perturbation reveals whether model performance is robust or reliant on superficial task-specific cues. Our approach is analogous to loss landscape sharpness: models overfit or contaminated for a single task (sharp minima) falter under task shifts, unlike models with generalizable solutions (flatter minima). We developed an automated pipeline with a calibrated judge scoring open-ended generations (captions, questions) using paraphrase and corruption sampling. Applying this framework to leading image/video MLLMs on benchmarks including MME, RealWorldQA, and CVRR-ES, we analyze each model's cross-task "ability vector." We demonstrate that fine-tuning on simulated test data (extreme contamination) drastically sharpens task-specific performance but harms overall generalization. Our dynamic task perturbation offers deeper insights into MLLM generalization, distinguishing genuine understanding from spurious leakage or overfitting.