bibtext: @inproceedings{
  hosseini2024vstar,
  title={V-{ST}aR: Training Verifiers for Self-Taught Reasoners},
  author={Arian Hosseini and Xingdi Yuan and Nikolay Malkin and Aaron Courville and Alessandro Sordoni and Rishabh Agarwal},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=stmqBSW2dV}
  }
abstract: Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al., 2022), iteratively fine-tune LLMs on self-generated solutions to improve their problem-solving ability. However, these approaches discard the large amounts of incorrect solutions generated during this process, potentially neglecting valuable information in such solutions. To address this shortcoming, we propose V-STaR that utilizes both the correct and incorrect solutions generated during the self-improvement process to train a verifier using DPO that judges correctness of model-generated solutions. This verifier is used at inference time to select one solution among many candidate solutions. Running V-STaR for multiple iterations results in progressively better reasoners and verifiers, delivering a 4% to 17% test accuracy improvement over existing self-improvement and verification approaches on common code generation and math reasoning benchmarks with LLaMA2 models.
title: V-STaR: Training Verifiers for Self-Taught Reasoners
authors: Arian Hosseini Xingdi Yuan Nikolay Malkin Aaron Courville Alessandro Sordoni Rishabh Agarwal