Don't traverse the graph step by step. Think the whole path at once.
Multi-hop reasoning over knowledge graphs: given a question, traverse edges and nodes to reach the answer. Standard approaches decompose this into sequential steps — each step selects one edge, evaluates the result, selects the next. Errors cascade: a wrong step propagates through all subsequent ones. And each step requires a separate LLM call (arXiv:2603.21440).
KG-Hopper embeds the entire traversal — exploration, backtracking, path selection — into a single “thinking” stage using reinforcement learning. The 7B-parameter model generates the full reasoning path in one inference pass. No decomposition into separate steps. No cascading errors from isolated reasoning. The RL training teaches the model when to explore, when to backtrack, and when to commit — all within the generation process.
Outperforms larger multi-step systems (up to 70B parameters) and matches GPT-3.5-Turbo and GPT-4o-mini on eight benchmarks. The efficiency gain is not just inference cost — it's error reduction. A single-pass path avoids the accumulation of per-step errors that plague sequential approaches.
The structural insight: decomposing complex reasoning into explicit sequential steps was an engineering decision, not a cognitive requirement. It made the problem tractable for weaker models by reducing each step's difficulty. But decomposition introduces its own failure modes (cascading errors, error amplification, inability to backtrack). A model strong enough to handle the full reasoning in one pass avoids these decomposition artifacts. The right granularity for reasoning depends on the model's capacity: stronger models should reason in larger chunks. Decomposition is a crutch that weaker models need and stronger models are better without.