Frontier models reason in target language after SFT, but benchmarks miss it

The paper's core finding is not that mixture-of-experts models work well on low-resource languages (they do), but that this success is almost entirely orthogonal to the benchmarks used to measure it. Models achieving 98% reasoning coverage show flat or marginal benchmark gains, suggesting current evaluation frameworks are measuring something fundamentally different from what matters in practice.

This connects directly to the August 18 GRPO unlearning study, which identified the same structural problem: optimization metrics and behavioral reality diverge. Just as that work showed models can appear unlearned on paper while retaining problematic knowledge, this research reveals models can be functionally competent at reasoning in a language while showing no signal in traditional accuracy tests. Both papers expose how reward misspecification and benchmark design create blind spots in deployment readiness. The difference here is scope: unlearning focused on knowledge suppression, while this addresses the broader question of whether benchmarks capture what users actually interact with.

If Alibaba, OpenAI, or NVIDIA release multilingual reasoning traces or user-auditable chain-of-thought datasets for low-resource languages in the next six months, that signals they're building evaluation infrastructure around this finding. If benchmark scores remain flat while reasoning coverage metrics become standard in their model cards, that confirms the paper's claim that existing metrics are the wrong target.