Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance
#Strategy executability #Mathematical reasoning #Selective Strategy Retrieval #Human-model differences #Example-based guidance #AIME25 #AI benchmarks #In-context learning
📌 Key Takeaways
- Researchers identified a critical gap between strategy usage and strategy executability in mathematical reasoning
- Human and AI strategies show systematic differences with complementary strengths
- The proposed SSR framework selectively combines strategies based on executability
- SSR achieved significant accuracy improvements across multiple benchmarks
📖 Full Retelling
Researchers Weida Liang, Yiyou Sun, Shuyuan Nan, Chuang Li, Dawn Song, and Kenji Kawaguchi published a groundbreaking paper on February 26, 2026, that addresses the instability in mathematical reasoning guidance systems, revealing that the effectiveness of example-based approaches varies significantly across problems and models due to an underexplored gap between strategy usage and strategy executability. The paper, 'Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance,' introduces the concept of strategy executability - whether a reasoning strategy remains effective when instantiated as guidance for a target model. Through controlled analysis of paired human-written and model-generated solutions, the researchers identified systematic differences between human- and model-derived strategies, which exhibit complementary strengths and source-dependent reversals under guidance. Building on their diagnosis, the researchers propose Selective Strategy Retrieval (SSR), a test-time framework that explicitly models executability by selectively retrieving and combining strategies using empirical, multi-route, source-aware signals. Across multiple mathematical reasoning benchmarks, SSR demonstrated consistent improvements over direct solving, in-context learning, and single-source guidance methods, achieving remarkable results with accuracy improvements of up to +13 points on AIME25 and +5 points on Apex for compact reasoning models.
🏷️ Themes
Artificial Intelligence, Mathematical Reasoning, Strategy Optimization
📚 Related People & Topics
Logical reasoning
Process of drawing correct inferences
Logical reasoning is a mental activity that aims to arrive at a conclusion in a rigorous way. It happens in the form of inferences or arguments by starting from a set of premises and reasoning to a conclusion supported by these premises. The premises and the conclusion are propositions, i.e.
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Logical reasoning:
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--> Computer Science > Artificial Intelligence arXiv:2602.22583 [Submitted on 26 Feb 2026] Title: Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance Authors: Weida Liang , Yiyou Sun , Shuyuan Nan , Chuang Li , Dawn Song , Kenji Kawaguchi View a PDF of the paper titled Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance, by Weida Liang and 5 other authors View PDF HTML Abstract: Example-based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems and models-even when the guidance is correct and problem-relevant. We show that this instability arises from a previously underexplored gap between strategy usage-whether a reasoning strategy appears in successful solutions-and strategy executability-whether the strategy remains effective when instantiated as guidance for a target model. Through a controlled analysis of paired human-written and model-generated solutions, we identify a systematic dissociation between usage and executability: human- and model-derived strategies differ in structured, domain-dependent ways, leading to complementary strengths and consistent source-dependent reversals under guidance. Building on this diagnosis, we propose Selective Strategy Retrieval , a test-time framework that explicitly models executability by selectively retrieving and combining strategies using empirical, multi-route, source-aware signals. Across multiple mathematical reasoning benchmarks, SSR yields reliable and consistent improvements over direct solving, in-context learning, and single-source guidance, improving accuracy by up to $+13$ points on AIME25 and $+5$ points on Apex for compact reasoning models. Code and benchmark are publicly available at: this https URL . Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL) Cite as: arXiv:2602.22583 [cs.AI] (or arXi...
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