Summary of Seed-cts: Unleashing the Power Of Tree Search For Superior Performance in Competitive Coding Tasks, by Hao Wang et al.
Seed-CTS: Unleashing the Power of Tree Search for Superior Performance in Competitive Coding Tasks
by Hao Wang, Boyi Liu, Yufeng Zhang, Jie Chen
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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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 paper addresses a significant challenge in large language models (LLMs) by proposing a novel token-level tree search method specifically designed for competition-level code generation. Currently, LLMs struggle to generate high-quality codes, with pass@1 rates as low as 0.143 on the LiveCodeBench-Hard dataset. The proposed approach leverages Qwen2.5-Coder-32B-Instruct and achieves a pass rate of 0.305 on the same dataset, surpassing GPT4o-0513’s performance. By integrating Chain-of-Thought (CoT) prompting, the method improves to 0.351, approaching O1-Mini’s pass@1 rate. The paper also reports the average number of generations required per problem by the tree search method on the test set. This work demonstrates the potential of tree search in enhancing performance on competition-level code generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to generate a piece of software from scratch, but without being able to write it yourself. That’s a challenge that current AI models are struggling with. In this paper, researchers propose a new way to help AI models do better at generating codes for complex problems. They tested their method on a difficult dataset and found that it performed much better than previous attempts. By adding an extra layer of guidance, the method was able to get even closer to achieving the same level of quality as human-written code. This breakthrough could lead to significant advancements in creating AI-generated software. |
Keywords
» Artificial intelligence » Prompting » Token
