Summary of Lmact: a Benchmark For In-context Imitation Learning with Long Multimodal Demonstrations, by Anian Ruoss et al.
LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations
by Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, Tim Genewein
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| 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 presents a comprehensive benchmark to evaluate the multimodal decision-making capabilities of frontier models, specifically Claude 3.5 Sonnet, Gemini 1.5 Flash, Gemini 1.5 Pro, Gemini 2.0 Flash Experimental, GPT-4o, o1-mini, o1-preview, and o1, in the very long-context regime (up to one million tokens). The authors investigate whether these models can learn from large numbers of expert demonstrations in their context by evaluating their performance across various interactive decision-making tasks. These tasks include playing tic-tac-toe, chess, and Atari games, navigating grid worlds, solving crosswords, and controlling a simulated cheetah. The authors study the effect of increasing amounts of expert demonstrations on model performance, finding that some models steadily improve with more demonstrations on certain tasks. They also explore the impact of encoding observations as text or images and the use of chain-of-thought prompting. To facilitate further innovation and evaluation, the authors open-source their benchmark, covering the zero-, few-, and many-shot regimes in a unified evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how advanced models make decisions. It’s like a big test to see if these models can learn from experts who show them how things are done. The test has lots of different challenges, like playing games, solving puzzles, and controlling robots. The authors want to know if the models can get better at these tasks when they’re shown more examples of how to do them correctly. They also look at what happens when they give the models different ways of thinking about the problems. Overall, this paper is important because it helps us understand how well our advanced models are doing and where we might need to improve them. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Prompting
