Why Extra Reasoning in AI Can Be Ineffective and Costly

Counterintuitive Findings in AI Reasoning

• 💡 The book "Building Agentic AI" explores surprising results from benchmarking AI reasoning models, specifically using the Math QA dataset.
• 📉 Contrary to expectations, there was no obvious correlation found between the level of reasoning and an LLM's performance on this dataset.
• ⚠️ While some benchmarks might show a correlation, it's often a marginal 1-2% increase in accuracy accompanied by a 2x increase in cost.

The Cost-Benefit of AI Reasoning

• 💰 The added cost of enabling reasoning in AI models often outweighs the marginal performance gains.
• 🛠️ It can be more effective to invest time in prompt engineering and building smaller, more succinct reasoning traces.
• ⚡ While not always the case, it's crucial to understand that increased reasoning is not a guarantee of better task performance.

Understanding Reasoning Models

• 🧠 Reasoning models, like OpenAI's 01, differ from typical LLMs by incorporating an internal review process before outputting a response.
• ⏳ This behind-the-scenes processing can lead to significant delays, with benchmarks taking hours for a small number of calls, contrasting with the seconds or minutes for immediate output models.
• 💬 The core mechanism of these reasoning models is still next token prediction, but they are trained to produce and review reasoning steps, which can sometimes be induced by simple prompts like "think through the problem before answering."

The Humorous Side of AI Reasoning

• 😂 An amusing experiment involved simply saying "hello" to reasoning models to observe the amount of reasoning they would produce in response.
• 📄 Some models generated extensive, multi-paragraph responses, attempting to infer user intent or task under the guise of a simple greeting, highlighting the potential for over-processing.