Work on packages brings the hard, sometimes controversial, topic: speed. Or
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2026-03-12 09:00:00
Surprisingly, I also found that despite the training reward being significantly higher, “best-of-N” distillation underperforms both CISPO and MCTS on the eval suite. While it’s not entirely clear why, we can theorise: if our model has a 98% chance of making at least one reasoning error during its thinking trace, there’s still a $1 - 0.98^{64} \approx 72.6 \%$ chance of selecting at least one correct trajectory. But if there’s no incentive to produce robust reasoning every time, it’s unlikely the model will learn to develop strategies that improve its single-shot score. In secondary school I used a number of techniques to keep track of intermediate steps when solving maths problems. This significantly reduced the probability of making “dumb mistakes” in exams. If I had the option to take the exam multiple times I would never have adopted those techniques!