VLMs Struggle with Videogame Data Annotation, Study Finds
A recent study published on arXiv examines the application of Vision Language Models (VLMs) for annotating sequences of frames in video games with reward signals, which could be useful in conditioned training and offline reinforcement learning. The research, entitled 'VLMs for Videogame Data Annotation', indicates that VLMs frequently have difficulty answering fundamental questions in racing games, a trend also seen across other gaming categories. The authors propose solutions like VLM output mixing and prompt optimization, while also demonstrating how factors such as input sequence length, resolution, and question batching influence the quality of annotations and token usage. This research underscores the difficulties of utilizing AI in synthetic environments that differ from real-world physics. The paper falls under Computer Science > Artificial Intelligence and can be accessed at arXiv:2608.05949.
Key facts
- The paper is titled 'VLMs for Videogame Data Annotation'.
- It was posted on arXiv with identifier 2608.05949.
- The study investigates using VLMs to annotate video game frame sequences with reward signals.
- Potential applications include conditioned training and offline reinforcement learning.
- VLMs often struggle with basic questions on racing video games.
- Similar behavior was observed in other game genres.
- Countermeasures include VLM output mixing and prompt optimization.
- Input sequence length, resolution, and question batching affect annotation quality and token consumption.
Entities
Institutions
- arXiv