Corrector Sampling in Language Models
Itai Gat, Neta Shaul, Uriel Singer, Yaron Lipman
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 $\sim$10\% relative improvements on reasoning and coding benchmarks compared to the standard sampling.