Diverse Reasoning Paths Matter: Symbolic Equivalence Augmentation for RL Exploration
In brief
- EquivAug creates diverse reasoning paths via verifiable symbolic transformations.
- Reasoning step diversity in fine-tuning boosts RLVR performance.
- Tests on GSM8K and FOLIO show a positive link between diversity and accuracy.
This study examines how the diversity of reasoning steps in supervised fine-tuning data impacts reinforcement learning with verifiable rewards (RLVR) performance. The authors introduce EquivAug, a data augmentation framework using symbolic equivalence transformations to generate varied yet correct reasoning paths.
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