Do prior conclusions on LM self-refinement hold for new reasoning models?

Ascertain whether conclusions from earlier analyses of large language model refinement (including claims of inability to self-correct without sophisticated feedback) remain valid for newly released reasoning-focused models such as DeepSeek-R1 and related systems.

Background

The authors note the emergence of new reasoning-oriented LMs that exhibit self-reflection patterns, raising the possibility that past findings on self-refinement may not generalize. Earlier studies largely targeted simpler or narrower benchmarks and feedback conditions.

RefineBench is designed to systematically evaluate these reasoning models under both self-refinement and guided refinement with controlled feedback, but the generalizability of prior conclusions to the new class remains an explicit uncertainty the paper aims to probe.