A Primer paper about how reasoning models improve after training
Rohan Paul Twitter · Rohan Paul (@rohanpaul_ai) · 2026-06-07
A new primer paper on reasoning model training finds that performance improvements depend less on raw data volume and more on verifiable feedback signals, challenging the assumption that Q&A pairs alone drive reasoning gains.
Extraction
Topics: reasoning-modelsmodel-trainingtraining-datarl-feedback
Claims
- Better reasoning models depend less on raw data size and more on checkable training evidence.
- Reasoning training data is not simple question-and-answer pairs; the useful component is feedback explaining why an answer is correct or incorrect.
- Verifiable feedback signals are the primary driver of reasoning model improvement.
Key quotes
better reasoning models depend less on raw data size and more on checkable training evidence.
reasoning data is NOT simple question-and-answer pairs. The useful part is often the feedback that says why [an answer is right or wrong].