Summary of Balrog: Benchmarking Agentic Llm and Vlm Reasoning on Games, by Davide Paglieri et al.
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
by Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, Tim Rocktäschel
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces BALROG, a novel benchmark for evaluating the agentic capabilities of Large Language Models (LLMs) and Vision Language Models (VLMs). The authors design a diverse set of challenging games that require intricate interactions, spatial reasoning, long-term planning, and continuous exploration. They evaluate several popular LLMs and VLMs using fine-grained metrics, finding that current models struggle with more challenging tasks, particularly in vision-based decision-making. The results suggest a need for further research and development in the agentic community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well computer models can make decisions and solve problems. These models are like super smart AI assistants that can understand language and images. But they still have trouble with really hard problems that require figuring things out and making plans. The researchers created a set of games that test these skills, and they found that the current models are not very good at solving these harder problems. They think this is because the models are not very good at using visual information to make decisions. |
