Summary of Openai-o1 Ab Testing: Does the O1 Model Really Do Good Reasoning in Math Problem Solving?, by Leo Li et al.
OpenAI-o1 AB Testing: Does the o1 model really do good reasoning in math problem solving?
by Leo Li, Ye Luo, Tingyou Pan
First submitted to arxiv on: 9 Nov 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 A comparison experiment was conducted to assess the logical reasoning capabilities of the Orion-1 model by OpenAI. The study aimed to investigate whether the model’s excellence is due to its ability to memorize solutions or if it can generalize well to unseen problems. Two datasets were used: one consisting of IMO problems and another consisting of CNT problems, which are similar in difficulty but less publicly accessible. The performance of the model was evaluated by labeling responses for each problem and comparing results between the two datasets. The findings suggest that there is no significant evidence to support the claim that the model relies on memorization. Additionally, case studies were conducted to analyze features of the model’s responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers tested a language model called Orion-1 from OpenAI. They wanted to see if it was good at solving math problems because it learned from examples or if it can solve new problems too. To do this, they used two sets of math problems: ones that are publicly available and another set that’s similar but not as well-known. The model’s answers were checked for each problem, and its performance was compared between the two sets. The results showed that Orion-1 does a good job solving new problems too, which means it’s not just memorizing old solutions. |
Keywords
» Artificial intelligence » Language model
