Summary of V-star: Training Verifiers For Self-taught Reasoners, by Arian Hosseini et al.
V-STaR: Training Verifiers for Self-Taught Reasoners
by Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, Rishabh Agarwal
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes an innovative approach to improve the problem-solving ability of large language models (LLMs). The authors suggest that current self-improvement methods, such as STaR, discard valuable information in incorrect solutions generated during the process. To address this limitation, they introduce V-STaR, a method that leverages both correct and incorrect solutions to train a verifier using DPO. This verifier is used at inference time to select one solution among many candidate solutions. The authors demonstrate the effectiveness of V-STaR by achieving 4% to 17% test accuracy improvement over existing methods on code generation and math reasoning benchmarks with LLaMA2 models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big language models better at solving problems. Right now, these models get better by practicing and correcting their mistakes. But they throw away all the wrong answers they come up with along the way. The authors of this paper think that might be a waste, because those wrong answers could still teach them something. So they created a new approach called V-STaR that uses both right and wrong answers to improve the model’s problem-solving skills. This makes the model better at picking the best answer from many possibilities. |
Keywords
* Artificial intelligence * Inference
