SELF-REWARDING CORRECTION FOR MATHEMATICAL REASONING

Summary from the TLDR AI newsletter

This paper is a study on self-rewarding reasoning in LLMs, which enables models to independently generate reasoning steps, self-assess correctness, and iteratively refine their outputs without external feedback. It proposes a two-stage training framework that combines sequential rejection sampling and reinforcement learning with rule-based signals which achieves self-correction performance competitive with methods requiring external reward models.