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arXiv · 2506.06215

Corrector Sampling in Language Models

Itai Gat·Neta Shaul·Uriel Singer·Yaron Lipman
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Abstract

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.

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Itai Gat, Neta Shaul, Uriel Singer, Yaron Lipman. 2025-06-06. Corrector Sampling in Language Models. https://arxiv.org/abs/2506.06215

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