Tool use enables LLMs to overcome reasoning collapse in OOD tasks

Researchers have developed a new benchmark to test the out-of-distribution (OOD) reasoning capabilities of large language models (LLMs). Their evaluations show that current LLMs, even frontier models, suffer from a significant collapse in reasoning accuracy as problem depth increases. However, the study demonstrates that enabling LLMs to synthesize, execute, and refine code through tool use can overcome this limitation, allowing smaller models to rival frontier performance on complex, long-horizon reasoning tasks. AI

IMPACT Tool use may be critical for developing LLMs capable of robust, long-horizon reasoning and generalization.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new benchmark and findings about LLM reasoning capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• CatalyzeX
• CORE Recommender
• DagsHub
• Hugging Face
• LLMs
• OOD reasoning
• Tomer Galanti
• tool use

• paper
• model release

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