Summary of A Minimalist Prompt For Zero-shot Policy Learning, by Meng Song et al.
A Minimalist Prompt for Zero-Shot Policy Learning
by Meng Song, Xuezhi Wang, Tanay Biradar, Yao Qin, Manmohan Chandraker
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 investigates how transformer-based methods can generalize to unseen tasks when prompted with minimal information. Currently, demonstrations or example solutions are used during inference, but it’s unclear what specific information is extracted from these prompts to aid generalization. In many real-world scenarios, access to demonstrations is impractical or unreasonable, especially in robotics applications. The authors explore the minimally sufficient prompt that can elicit the same level of generalization ability as demonstrations. They use contextual RL setting, which allows for quantitative measurement of generalization and is commonly used in meta-RL and multi-task RL benchmarks. The training and test MDPs only differ in certain properties, referred to as task parameters. Conditioning a decision transformer on these task parameters alone enables zero-shot generalization comparable to or better than its demonstration-conditioned counterpart. This suggests that task parameters are essential for generalization, and DT models try to recover this information from the prompt. To extract remaining generalizable information, an additional learnable prompt is introduced, which further boosts zero-shot generalization across robotic control, manipulation, and navigation benchmark tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can learn new skills without needing lots of examples or demonstrations. Right now, machines are good at learning when they’re shown what to do, but it’s not clear why this works. In real life, it might be hard to get all the examples you need, especially in places like robots. The researchers want to find out what’s the bare minimum information you need to teach a machine something new. They use a special kind of testing that lets them measure how well machines generalize, or apply what they’ve learned to new situations. They found that giving machines specific details about the task helps them learn without needing examples, which is good news for things like robots and self-driving cars. |
Keywords
» Artificial intelligence » Generalization » Inference » Multi task » Prompt » Transformer » Zero shot
