<ul><li>Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems.</li><li>This research investigates the benefits and limitations of scaling methods across nine state-of-the-art models and eight challenging tasks.</li><li>The advantages of inference-time scaling vary across tasks and diminish as problem complexity increases.</li><li>Results show that, for some tasks, conventional models can achieve performance close to advanced reasoning models, but for other tasks, a performance gap remains.</li></ul>

Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead

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