Multi-step Reasoning

Multi-step reasoning — maintaining coherent inference chains across 5+ sequential steps — is the meta-capability that determines whether a model can solve complex real-world problems or only handle one-hop questions. Benchmarks like StrategyQA, MuSiQue, and BIG-Bench Hard isolate this ability, and the performance gap between single-step and multi-step tasks remains the widest failure mode of current LLMs. Techniques like chain-of-thought, tree-of-thought, and iterative refinement help, but error accumulation across steps means that 95% per-step accuracy yields only 60% accuracy over 10 steps — a fundamental scaling challenge.