Autoregressive language models accumulate errors due to their fixed, irrevocable left-to-right token generation. To address this, we propose a new sampling method called Resample-Previous-Tokens (RPT). RPT mitigates error accumulation by iteratively revisiting and potentially replacing tokens in a window of previously generated text. This method can be integrated into existing autoregressive models, preserving their next-token-prediction quality and speed. Fine-tuning a pretrained 8B parameter model with RPT for only 100B resulted in ~10% relative improvements on reasoning and coding benchmarks compared to the standard sampling.
@article{arxiv.2506.06215,
title = {Corrector Sampling in Language Models},
author = {Itai Gat and Neta Shaul and Uriel Singer and Yaron Lipman},
journal= {arXiv preprint arXiv:2506.06215},
year = {2025}
}