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 independently, removing prior attempts from the context.
The process uses no reward, verifier, or correctness filter.
On HumanEval+, MBPP+, and DS-1000, DRY-SFT raises pass@100 by 10.8, 12.5, and 12.4 percentage points, respectively, at a small cost to pass@1.
Structural diversity, measured by abstract syntax tree edit distance among passing solutions, rises significantly on all three benchmarks.
DRY-SFT also solves 244 of 600 problems that the base model did not solve in the same 200 attempts.
Across nine open-weight models, lower structural diversity of the base model significantly predicts larger DRY-SFT gains, indicating that the method is especially effective on more mode-collapsed models.