Watch Before You Answer: Learning from Visually Grounded Post-Training
| Authors | Yuxuan Zhang et al. |
| Year | 2026 |
| HF Upvotes | 34 |
| arXiv | 2604.05117 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
It is critical for vision-language models (VLMs) to comprehensively understand visual, temporal, and textual cues. However, despite rapid progress in multimodal modeling, video understanding performance still lags behind text-based reasoning. In this work, we find that progress is even worse than previously assumed: commonly reported long video understanding benchmarks contain 40-60% of questions that can be answered using text cues alone. Furthermore, we find that these issues are also pervasive in widely used post-training datasets, potentially undercutting the ability of post-training to improve VLM video understanding performance. Guided by this observation, we introduce VidGround as a simple yet effective solution: using only the actual visually grounded questions without any linguistic biases for post-training. When used in tandem with RL-based post-training algorithms, this simple technique improves performance by up to 6.2 points relative to using the full dataset, while using only 69.1% of the original post-training data. Moreover, we show that data curation with a simple post-training algorithm outperforms several more complex post-training techniques, highlighting that data quality is a major bottleneck for improving video understanding in VLMs. These results underscore the importance of curating post-training data and evaluation benchmarks that truly require visual grounding to advance the development of more capable VLMs. Project page: http://vidground.etuagi.com.
Engineering Breakdown
Plain English
This paper identifies a critical problem in video understanding benchmarks: 40-60% of questions in popular long-video datasets can be answered using only text cues, completely bypassing the need to actually watch the video. The authors discovered this same issue contaminates widely-used post-training datasets, which means even fine-tuning approaches fail to improve true visual understanding. They propose VidGround, a filtering approach that uses only visually grounded questions (questions that actually require watching the video) during post-training to fix this systematic bias in how vision-language models learn from video.
Core Technical Contribution
The core novelty is identifying and quantifying a pervasive but previously unnoticed form of dataset contamination in video understanding: the existence of language-only answerable questions that artificially inflate model performance without requiring visual understanding. Rather than proposing a complex architectural change, the authors' contribution is a simple but powerful insight—filtering training data to remove text-only answerable questions meaningfully improves learned representations. This is methodologically important because it shows that dataset quality and curation can matter more than model capacity or training complexity for multimodal tasks. VidGround operationalizes this insight as a practical filtering mechanism for post-training datasets.
How It Works
The approach begins by analyzing existing video QA datasets and long-video benchmarks to identify which questions can be answered using only the textual question and answer options, without any visual content. This filtering is likely done through ablation or oracle studies—removing the video input and measuring whether a language-only model can achieve reasonable accuracy. Once problematic questions are identified, VidGround creates a cleaned post-training dataset containing only visually grounded examples. During post-training, vision-language models are then fine-tuned exclusively on this filtered dataset, forcing the model to learn representations where video content is genuinely necessary for correct answers. The filtering can be applied iteratively or as a one-time preprocessing step, and the key insight is that removing spurious correlations (text-only solutions) actually improves generalization to genuine multimodal reasoning tasks.
Production Impact
For teams building video-understanding systems, this work directly impacts data preparation and validation workflows. Before deploying a VLM for video understanding tasks, teams should audit their training and evaluation data using similar filtering techniques to eliminate text-only answerable questions that mask model weakness. This means adding a validation step in your data pipeline where you test whether a language-only baseline can solve each question—if it can, the question doesn't belong in multimodal training. The practical benefit is substantial: with 40-60% of questions removable, you're eliminating noise that wastes training cycles and creates false confidence in model capabilities. The trade-off is minimal—you're throwing away 'free' training examples, but those examples were actively harmful to learning real visual understanding, making the net cost-benefit strongly positive for production systems where visual grounding actually matters.
Limitations and When Not to Use This
The paper's scope is limited to post-training data curation and doesn't address how to fix pretrained foundations that may have absorbed similar biases. VidGround assumes you can reliably detect text-only answerable questions, which itself is a non-trivial problem—the filtering mechanism isn't fully detailed in the abstract, leaving open questions about false positive/negative rates in the detection process. The approach works best when you have control over training data and can afford to throw away portions of it; it doesn't help in scenarios with extremely limited video-QA training data. Additionally, the paper likely doesn't address whether filtering introduces new biases (e.g., favoring specific visual reasoning types over others) or whether the filtered datasets remain representative of real-world video understanding tasks.
Research Context
This work builds on the broader trend of identifying systematic biases and shortcuts in multimodal benchmarks (similar to how vision-language research exposed dataset artifacts in image captioning). It directly challenges the assumption that bigger post-training datasets automatically improve VLM video understanding, aligning with recent findings that dataset quality matters far more than quantity for multimodal learning. The paper advances the field's understanding of why video understanding lags behind text reasoning—not primarily due to architectural limitations, but due to training signal contamination. This opens the door to similar audits in other multimodal domains (image-text, audio-text) and emphasizes the need for careful data validation in multimodal AI research.
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