AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?
This work addresses a critical gap in benchmarking for AI researchers and developers by exposing fundamental limitations in LRMs' algorithmic reasoning, which is incremental as it builds on existing benchmarks but introduces a new taxonomy and analysis.
The paper tackles the problem of evaluating whether Large Reasoning Models (LRMs) truly master algorithmic reasoning by proposing AlgBench, an expert-curated benchmark with over 3,000 problems across 27 algorithms, revealing substantial performance heterogeneity with accuracy dropping from up to 92% on non-optimized tasks to around 49% on globally optimized algorithms like dynamic programming.
Reasoning ability has become a central focus in the advancement of Large Reasoning Models (LRMs). Although notable progress has been achieved on several reasoning benchmarks such as MATH500 and LiveCodeBench, existing benchmarks for algorithmic reasoning remain limited, failing to answer a critical question: Do LRMs truly master algorithmic reasoning? To answer this question, we propose AlgBench, an expert-curated benchmark that evaluates LRMs under an algorithm-centric paradigm. AlgBench consists of over 3,000 original problems spanning 27 algorithms, constructed by ACM algorithmic experts and organized under a comprehensive taxonomy, including Euclidean-structured, non-Euclidean-structured, non-optimized, local-optimized, global-optimized, and heuristic-optimized categories. Empirical evaluations on leading LRMs (e.g., Gemini-3-Pro, DeepSeek-v3.2-Speciale and GPT-o3) reveal substantial performance heterogeneity: while models perform well on non-optimized tasks (up to 92%), accuracy drops sharply to around 49% on globally optimized algorithms such as dynamic programming. Further analysis uncovers \textbf{strategic over-shifts}, wherein models prematurely abandon correct algorithmic designs due to necessary low-entropy tokens. These findings expose fundamental limitations of problem-centric reinforcement learning and highlight the necessity of an algorithm-centric training paradigm for robust algorithmic reasoning.