Guide, Don’t Guess: Reallocating Reasoning Compute with Small-Model Hints
TL;DR for operators When a compact model fails on a difficult multi-step problem, the default remedies are expensive: use a larger model, generate many independent attempts, or accept lower accuracy. This paper tests a different allocation of compute—keep the solver small, but give it localized guidance at difficult intermediate steps. HintMR1 separates those jobs. A hinter tells the solver what to consider next without supplying the full solution; the solver then advances its reasoning one step at a time. On AIME-2024, DeepSeek-R1-Distill-Qwen-7B rises from 20.69% accuracy without hints to 68.97% with GPT-5.2-generated hints. The result is not evidence that any second model helps: non-fine-tuned small-model hints are inconsistent and sometimes reduce accuracy below the no-hint baseline. ...