We present Fikiria, a two-stage SFT+GRPO recipe for Swahili mathematical reasoning.
Its reward multiplies a format gate by a weighted sum of answer accuracy and the ChrF++ similarity of the Swahili reasoning to an in-language reference chain of thought, so that training targets the surface fidelity of the reasoning trace and not only the final answer.
We train on a 2.3k-problem Swahili dataset created by translating GSM8K with GPT-4.1 and filtering with SSA-COMET.
On AfriMGSM Swahili, Fikiria-AfriqueQwen-14B reaches 79.2%.
Under the same fixed prompt and strict answer-format scoring, GPT-4o reaches 68.0%, partly because it often fails the required answer format; IrokoBench reports 78.8% for GPT-4o using a best-per-model prompt, a figure numerically close to ours but not directly comparable.
In a small human evaluation with co-author annotators, GPT-4o's outputs were preferred overall.
ChrF++ measures surface fidelity, not the validity of individual reasoning steps.