Don't Repeat Yourself: Self-Supervised Fine-Tuning for Coverage
In verifiable domains such as math and coding, finding one correct solution among many attempts can matter more than the pass rate of each attempt. Post-training can concentrate large language model outputs around a few modes, while increasing sampling temperature has limited effectiveness. We introduce Don't Repeat Yourself Supervised Fine-Tuning (DRY-SFT), a post-training method that increases output diversity and coverage: the probability of at least one correct solution among many attempts. DRY-SFT has two stages. First, for each problem, sequentially generate K solutions, showing the model all prior attempts and asking for a different solution. Second, fine-tune on each attempt indep...
| Authors | Eric Fithian, Kirill Skobelev, X. Y. Han |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 30 September 2026 |
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