Corrector Sampling in Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912417421721600 |
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| author | Gat, Itai Shaul, Neta Singer, Uriel Lipman, Yaron |
| author_facet | Gat, Itai Shaul, Neta Singer, Uriel Lipman, Yaron |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Corrector Sampling in Language Models Gat, Itai Shaul, Neta Singer, Uriel Lipman, Yaron Machine Learning Computation and Language 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. |
| title | Corrector Sampling in Language Models |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2506.06215 |