Summary of Rejecting Hallucinated State Targets During Planning, by Mingde Zhao et al.
Rejecting Hallucinated State Targets during Planning
by Mingde Zhao, Tristan Sylvain, Romain Laroche, Doina Precup, Yoshua Bengio
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposes an innovative solution to address the limitations of generative models in planning by introducing a target evaluator to reject hallucinated, infeasible targets. The model can struggle with proposing unrealistic targets, leading to safety concerns. To mitigate this issue, the authors suggest combining learning rules, architectures, and novel hindsight relabeling strategies to produce accurate evaluations. Their experiments demonstrate that this approach significantly reduces delusional behaviors and improves planning agent performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer models to help machines make good decisions. These models can sometimes suggest crazy or impossible things for the machine to do, which is a problem because it might not be safe. The researchers came up with an idea to add a new part to the model that checks if the suggested actions are realistic before letting the machine do them. They tested this approach and found that it makes the machines make better decisions and avoid doing silly things. |
