FSLR

TL;DR for operators A post-training team with a fixed budget can spend it on long worked solutions or on shorter labels aimed at the decision that determines the rest of the reasoning path. Wang and Zhang’s study1 provides evidence that, for mathematical word problems, the second option deserves serious consideration. The authors classify more than 90% of analyzed model failures as mistakes in selecting the relevant quantities, dependencies, or operation. Those errors remain dominant after conventional chain-of-thought supervised fine-tuning. Their alternative, First-Step Logical Reasoning (FSLR), trains the model only on that initial planning decision. Across the reported experiments, it generally beats matched full-trajectory fine-tuning, including under distribution shift, while using roughly 81-87% fewer supervised training tokens and approximately four- to sixfold less training time. ...