Reasoning Models Can Be Effective Without Thinking
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This paper challenges the necessity of long chain-of-thought (CoT) reasoning in LLMs by introducing a simple prompting method called NoThinking, which bypasses explicit "thinking" steps. Surprisingly, NoThinking performs comparably to or better than traditional reasoning under comparable or even lower compute budgets, especially when paired with parallel decoding and best-of-N selection. Key Insights:
NoThinking prepends a dummy “Thinking” block and jumps straight to final answers.
Despite skipping structured reasoning, it outperforms Thinking in pass@k (1–64) on many benchmarks, especially under token constraints.
With parallel scaling, NoThinking achieves higher pass@1 accuracy than Thinking while using 4× fewer tokens and up to 9× lower latency.
Tasks evaluated: competitive math (AIME24/25, AMC23, OlympiadBench), coding (LiveCodeBench), and formal theorem proving (MiniF2F, ProofNet).
NoThinking is shown to provide superior accuracy–latency tradeoffs and generalizes across diverse tasks. Results:
Low-budget wins: On AMC23 (700 tokens), NoThinking achieves 51.3% vs. 28.9% (Thinking).
Better scaling: As k increases, NoThinking consistently surpasses Thinking.
Efficiency frontier: Across benchmarks, NoThinking dominates the accuracy–cost Pareto frontier.
Parallel wins: With simple confidence-based or majority vote strategies, NoThinking + best-of-N beats full Thinking on pass@1 with significantly less latency.
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