Six training strategies compared: can LLMs reason over graphs at scale?
burkov · x · 2026-10-14
- A systematic comparison of training strategies for LLM graph reasoning, asking whether training yields reliable reasoning as graphs grow or change—critical for combining graph data with text where long textual prompts are inefficient.
- Evaluates 6 approaches on synthetic directed graphs: supervised fine-tuning, prompt tuning, two RL variants, and graph-encoder tokens used alone or alongside graph text.
- Trains on 4 basic tasks (node counting, degree, two reachability forms) on 5–20 node graphs; tests generalization on larger graphs, unseen structures, and related untrained tasks.
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