Summary of Alphamath Almost Zero: Process Supervision Without Process, by Guoxin Chen et al.
AlphaMath Almost Zero: Process Supervision without Process
by Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Recent advancements in large language models (LLMs) have significantly improved their performance on various tasks. However, they still face challenges with complex and symbolic multi-step reasoning, particularly in mathematical reasoning. To address this challenge, our proposed framework, AlphaMath, leverages Monte Carlo Tree Search (MCTS) to bypass the need for process annotations from humans or GPTs. We integrate a value model with the LLM to generate both process supervision and step-level evaluation signals in MCTS. Additionally, we propose an efficient inference strategy, step-level beam search, which assists the policy model (i.e., LLM) in navigating more effective reasoning paths. Our experimental results demonstrate that AlphaMath achieves comparable or superior results to previous state-of-the-art methods on both in-domain and out-of-domain datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study is about making large language models better at doing math problems. Right now, these models are good at some things, but they struggle with more complex math problems that require multiple steps. To help them do better, we created a new way of training the models called AlphaMath. Instead of needing humans or special computers to tell us how to solve the math problems, AlphaMath uses a technique called Monte Carlo Tree Search. This allows the model to figure out how to solve the problem on its own. We tested our approach and found that it works just as well as other methods that use more human help. |
Keywords
» Artificial intelligence » Inference
