Summary of Enhancing Llm Reasoning with Reward-guided Tree Search, by Jinhao Jiang et al.
Enhancing LLM Reasoning with Reward-guided Tree Search
by Jinhao Jiang, Zhipeng Chen, Yingqian Min, Jie Chen, Xiaoxue Cheng, Jiapeng Wang, Yiru Tang, Haoxiang Sun, Jia Deng, Wayne Xin Zhao, Zheng Liu, Dong Yan, Jian Xie, Zhongyuan Wang, Ji-Rong Wen
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Recently, test-time scaling has garnered significant attention in the research community, driven by OpenAI’s o1 model advancements. By allocating more computational resources during inference, large language models (LLMs) can extensively explore the solution space, generating more thought tokens or diverse solutions for more accurate responses. However, developing an o1-like reasoning approach remains challenging, prompting researchers to explore various attempts to advance this open area of research. This paper presents a preliminary exploration into enhancing LLMs’ reasoning abilities through reward-guided tree search algorithms. The framework integrates the policy model, reward model, and search algorithm, denoted as STILL-1. We thoroughly explore design considerations and provide technical details. To evaluate our approach, we focus on mathematical reasoning tasks and conduct extensive evaluations on four challenging datasets, significantly enhancing LLMs’ reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make big language models (LLMs) better at solving problems. Right now, these models are really good at doing things like answering questions or translating languages, but they can struggle when faced with harder problems that require more thought and creativity. To help solve this problem, the researchers in this paper have developed a new approach called STILL-1. This approach uses a combination of techniques to guide the model through different possibilities, kind of like how humans might use reasoning and logic to come up with solutions. The team tested their approach on four challenging datasets and found that it worked really well! |
Keywords
» Artificial intelligence » Attention » Inference » Prompting
