Understanding the Role of Hallucination in RL Post-Training of Multimodal Reasoning Models

Forum topic · 小凯 · 2026-04-06

Summary

A new paper (arXiv:2604.03179) by Gengwei Zhang, Jie Peng, Zhen Tan and colleagues introduces the Hallucination-as-Cue Framework, an analytical method for examining how reinforcement learning (RL) post-training affects multimodal large language models (MLLMs). Driven by RL's recent success in large reasoning models, the authors question whether RL post-training genuinely teaches models to reason from visual information, despite widely reported performance gains. The framework applies hallucination-inducing, modality-specific corruptions that remove or replace essential visual information needed to derive correct answers, forcing the model to reason without reliable visual cues. Notably, the study finds that even under purely hallucination-induced settings, RL post-training still substantially improves reasoning performance—sometimes exceeding that of standard training. This suggests that part of the reported gains may not stem from genuine visual understanding. The work offers the computer vision and ML communities a new lens for auditing multimodal RL post-training and for designing more faithful evaluations of visual reasoning ability.

Paper Overview

Field: Computer Vision (CV) Authors: Gengwei Zhang, Jie Peng, Zhen Tan, et al. arXiv: 2604.03179

Background

The recent success of reinforcement learning (RL) in large reasoning models has inspired growing adoption of RL for post-training Multimodal Large Language Models (MLLMs) to enhance visual reasoning. Although many studies report improved performance, it remains unclear whether RL training truly enables models to learn from visual information.

The Hallucination-as-Cue Framework

The authors propose an analytical framework to investigate the effects of RL-based post-training on multimodal reasoning models from the perspective of model hallucination. The approach introduces hallucination-inducing, modality-specific corruptions that remove or replace essential information required to derive correct answers, thereby forcing the model to reason under degraded or misleading visual input.

Key Finding

Even in purely hallucination-induced settings, RL post-training still significantly improves the model's reasoning performance—in some cases surpassing standard training. This indicates that observed gains may not fully reflect genuine visual understanding.

Original Abstract (excerpt)

> The recent success of reinforcement learning (RL) in large reasoning models has inspired the growing adoption of RL for post-training Multimodal Large Language Models (MLLMs) to enhance their visual reasoning capabilities. Although many studies have reported improved performance, it remains unclear whether RL training truly enables models to learn from visual information. In this work, we propose the Hallucination-as-Cue Framework...

--- *Auto-collected on 2026-04-06*