Summary of Vlm Agents Generate Their Own Memories: Distilling Experience Into Embodied Programs Of Thought, by Gabriel Sarch et al.
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought
by Gabriel Sarch, Lawrence Jang, Michael J. Tarr, William W. Cohen, Kenneth Marino, Katerina Fragkiadaki
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to few-shot learning is proposed, where a large-scale vision language model (VLM) iteratively refines suboptimal trajectories into high-quality data through optimized actions and detailed reasoning. This In-Context Abstraction Learning (ICAL) method enables the VLM to correct actions, annotate causal relationships, object states, subgoals, and task-relevant visuals, forming “programs of thought.” With human feedback, these programs are improved as the agent executes them in a similar environment. The resulting examples can be used as prompt context or fine-tuning data, significantly boosting decision-making while reducing human feedback needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large-scale language models and vision language models do well with few-shot learning but need high-quality examples. Researchers came up with a new way to create these good examples by making the model improve its own understanding of how things work. They called this “In-Context Abstraction Learning” or ICAL for short. The model makes mistakes, corrects them, and adds more details, kind of like taking notes while it’s learning. This helps the model learn from its own experiences and make better decisions without needing as much human help. |
Keywords
» Artificial intelligence » Boosting » Few shot » Fine tuning » Language model » Prompt
