Summary of Guided Stream Of Search: Learning to Better Search with Language Models Via Optimal Path Guidance, by Seungyong Moon et al.
Guided Stream of Search: Learning to Better Search with Language Models via Optimal Path Guidance
by Seungyong Moon, Bumsoo Park, Hyun Oh Song
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The proposed work explores ways to improve language models’ search and planning abilities by incorporating optimal solutions into their self-generation process. The method, called guided stream of search (GSoS), uses progressive integration to produce high-quality search trajectories that are then fine-tuned via supervised learning. This approach leads to significant enhancements in the models’ performance on mathematical reasoning tasks like Countdown. When combined with reinforcement learning (RL) fine-tuning, GSoS achieves even better results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models can be great at some things, but they still struggle with planning and reasoning. Researchers tried teaching them by letting them search for answers, which helped a little. But then they realized that optimal solutions could be like stepping stones to help the model find better answers. This new approach combines optimal solutions with the searching process in a way that makes sense, and it really helps the model get better at planning and problem-solving. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning » Supervised
