SLR: Automated Synthesis for Scalable Logical Reasoning

• URL: http://arxiv.org/abs/2506.15787v4
• Date: Wed, 06 Aug 2025 15:09:52 GMT
• Title: SLR: Automated Synthesis for Scalable Logical Reasoning
• Authors: Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia Wüst, Hikaru Shindo, Rupert Mitchell, Tim Woydt, Patrick Schramowski, Wolfgang Stammer, Kristian Kersting,
• Abstract summary: We introduce SLR, an end-to-end framework for systematic evaluation and training of Large Language Models (LLMs)<n>Given a user's task specification, SLR automatically synthesizes an instruction prompt for an inductive reasoning task.<n>Using SLR, we create SLR-Bench, a benchmark comprising 19k prompts organized into 20 curriculum levels.
• Score: 23.14914698597022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce SLR, an end-to-end framework for systematic evaluation and training of Large Language Models (LLMs) via Scalable Logical Reasoning. Given a user's task specification, SLR automatically synthesizes (i) an instruction prompt for an inductive reasoning task, (ii) a validation program, executable on model outputs to provide verifiable rewards, and (iii) the latent ground-truth rule. This process is fully automated, scalable, requires no human annotations, and offers precise control over task difficulty. Using SLR, we create SLR-Bench, a benchmark comprising 19k prompts organized into 20 curriculum levels that progressively increase in relational, arithmetic, and recursive complexity. Large-scale evaluation reveals that contemporary LLMs readily produce syntactically valid rules, yet often fail at correct logical inference. Recent reasoning LLMs demonstrate improved performance but incur very high test-time computation, with costs exceeding $300 for just 1,000 prompts. Finally, curriculum learning via SLR doubles Llama-3-8B accuracy on SLR-Bench, achieving parity with Gemini-Flash-Thinking at a fraction of computational cost. Moreover, these reasoning capabilities generalize to a wide range of established benchmarks, underscoring the effectiveness of SLR for downstream reasoning.

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