Zovda
A model trained on long solutions learns to write long solutions. Benchmarks that reward length keep rising while accuracy moves sideways, which is the tell.
Reasoning data is not prose volume. It is a record of decisions.
Keep the branch that was discarded
The most informative step in a solution is often the one that was tried and abandoned. It says which approach looked plausible and why it failed.
A corpus that only stores the successful path teaches confidence without discrimination.
Separate reasoning from prose
If the chain and the final explanation are one field, a model cannot learn to think concisely and answer politely at the same time.
We keep three fields: the working steps, the final answer, and a short rationale. Each is trained on its own schedule.
Verify the steps, not only the result
A correct answer with a broken chain teaches the wrong process. Check intermediate values where the task allows it, and mark rows where only the final answer is checkable.
That mark prevents a buyer from over-weighting rows that look rigorous but cannot be audited.
The checklist we run
- Does every row separate working steps from the final answer?
- Are rejected branches recorded where available?
- Are intermediate steps checkable, and marked when they are not?
- Is reasoning length independent of answer length?
- Can a row be traced to the task it solves?
If a supplier cannot answer these, the corpus teaches format. Zovda builds reasoning families around decision chains, and the Datuva catalogue keeps them distinct from plain question-answer rows.
Where to start
Start from the task type: arithmetic, code, planning, or extraction. Each needs a different chain depth. The question bank collection covers the reasoning-heavy end, and the corpus catalogue maps it to stages.
Tell us the task and the volume. We reply within one business day.