SAPO: Self-Adaptive Process Optimization Makes Small Reasoners Stronger
This work improves self-evolution methods for small language models, with domain-specific applications in mathematics and coding.
The paper tackles the problem of self-improvement in Small Language Models by addressing the reasoner-verifier gap and computational inefficiency of Monte Carlo process supervision, proposing SAPO which outperforms existing methods on mathematics and code tasks.
Existing self-evolution methods overlook the influence of fine-grained reasoning steps, which leads to the reasoner-verifier gap. The computational inefficiency of Monte Carlo (MC) process supervision further exacerbates the difficulty in mitigating the gap. Motivated by the Error-Related Negativity (ERN), which the reasoner can localize error following incorrect decisions, guiding rapid adjustments, we propose a Self-Adaptive Process Optimization (SAPO) method for self-improvement in Small Language Models (SLMs). SAPO adaptively and efficiently introduces process supervision signals by actively minimizing the reasoner-verifier gap rather than relying on inefficient MC estimations. Extensive experiments demonstrate that the proposed method outperforms most existing self-evolution methods on two challenging task types: mathematics and code. Additionally, to further investigate SAPO's impact on verifier performance, this work introduces two new benchmarks for process reward models in both mathematical and coding tasks.