V-STaR: Training Verifiers for Self-Taught Reasoners
<|reference_start|>V-STaR: Training Verifiers for Self-Taught Reasoners: Common self-improvement approaches for large language models (LLMs), such as STaR, 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.<|reference_end|>
@article{hosseini2024v-star:,
    title={V-STaR: Training Verifiers for Self-Taught Reasoners},
    author={Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville,
  Alessandro Sordoni and Rishabh Agarwal},
    journal={arXiv preprint arXiv:2402.06457},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06457},
    primaryClass={cs.LG cs.AI cs.CL}
}