pipeline_tag: text-ranking
library_name: transformers
license: apache-2.0
Budget-Aware Test-Time Scaling via Discriminative Verification
This repository contains the verification-1.5b model, implementing the discriminative verifier described in the paper:
Budget-Aware Test-Time Scaling via Discriminative Verification
📃 [Paper on arXiv] • 💻 [GitHub] • 🤗 [Hugging Face Collection]
Model Description
This model is a discriminative verifier designed for budget-aware test-time scaling in large language models on complex reasoning tasks. While state-of-the-art approaches often employ generative verifiers, this model shifts focus to a more budget-aware paradigm: discriminative verification. Empirical analysis demonstrates that combining discriminative verifiers with self-consistency creates a powerful and efficient test-time scaling mechanism, surpassing state-of-the-art generative verification under a fixed compute budget. This approach is not only a "free" upgrade over self-consistency but also a more effective and efficient alternative to costly generative techniques for practical, real-world applications.
Usage
You can use this model to score candidate solutions for various evaluation datasets.
First, ensure you have the necessary environment set up by installing the package:
conda create -n verification python=3.10
conda activate verification
pip install -e . # This installs the 'verification' package and its dependencies
Then, you can run verification on an evaluation dataset (e.g., aime2024, aime2025, livebench-math, or gpqa) with the trained verifier using the run_judge_hf.py script:
python scripts/run_judge_hf.py \
--model_name "WangResearchLab/verification-1.5b" \
--dataset_name "verification-evaluation-data" \
--dataset_split "validation" \
--save_path "evals/verification-1.5b/validation-eval.jsonl" \
--num_gpus 8
This command will score candidate solutions with the WangResearchLab/verification-1.5b model and save the evaluation results to the specified path.
Citation
If you find this work useful, please consider citing the paper:
@article{montgomery2025budget,
title={Budget-Aware Test-Time Scaling via Discriminative Verification},
author={Montgomery, Kyle and Tan, Sijun and Chen, Yuqi and Zhuang, Siyuan and Zhang, Tianjun and Popa, Raluca Ada and Wang, Chenguang},
journal={arXiv preprint arXiv:2510.14913},
year={2025}
}