LIMO: Less is More for Reasoning
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About LIMO: Less is More for Reasoning
LIMO (Less is More for Reasoning) is a research model that demonstrates sophisticated mathematical reasoning in large language models can emerge with minimal training data. Through simple supervised fine-tuning, LIMO achieves state-of-the-art results on AIME24 (63.3%) and MATH500 (95.6%), outperforming previous models that used 100x more data. It proposes the Less-Is-More Reasoning Hypothesis, suggesting that complex reasoning can be elicited with few cognitive template demonstrations if the model has a comprehensive pre-trained knowledge base.
Key Features
Achieves 63.3% accuracy on AIME24 with minimal training data
95.6% accuracy on MATH500
Uses only 1% of training data compared to prior approaches
Strong out-of-distribution generalization (45.8% absolute improvement across benchmarks)
Proposes Less-Is-More Reasoning Hypothesis
Based on simple supervised fine-tuning
Pros & Cons
Pros
- Requires significantly less training data than previous methods
- Achieves high accuracy on challenging math benchmarks
- Exhibits strong generalization to unseen tasks
- Provides theoretical hypothesis for reasoning emergence
Cons
- Focuses primarily on mathematical reasoning; may not generalize to other domains
- Requires a strong pre-trained foundation model with comprehensive knowledge
- Limited to supervised fine-tuning; not tested with other methods
Best For
Mathematical reasoning tasksLLM fine-tuning researchDemonstrating data efficiency in reasoningOut-of-distribution generalization benchmarks
FAQ
What is the LIMO hypothesis?
The Less-Is-More Reasoning Hypothesis states that in foundation models where domain knowledge is comprehensively encoded during pre-training, sophisticated reasoning can emerge through minimal but strategically designed demonstrations of cognitive processes.
How much training data does LIMO use?
LIMO uses only 1% of the training data required by previous fine-tuned models.