Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning
Test-time scaling improves LLM reasoning by using additional inference compute, but wider sampling alone can suffer from diminishing returns: new rollouts often repeat existing answer patterns instead of adding useful reasoning diversity. Verifier-based selection offers an alternative, but its performance depends on the calibration of an external reward model. We propose a verifier-free breadth--depth refinement framework that uses test-time compute to both explore and improve candidate solutions. The method samples multiple independent reasoning rollouts, refines each rollout through iterative self-critique and self-correction, and aggregates the refined answers by majority voting. Bread...
| Authors | Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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