AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?

TL;DR

This paper introduces AlgBench, a comprehensive benchmark for evaluating large reasoning models' understanding of algorithms, revealing significant performance gaps and strategic limitations in current models.

Contribution

We present AlgBench, a new expert-curated benchmark with over 3,000 problems across 27 algorithms, to assess and analyze the algorithmic reasoning capabilities of large models.

Findings

Models perform well on non-optimized tasks (up to 92%)

Accuracy drops to around 49% on globally optimized algorithms

Models exhibit strategic over-shifts, prematurely abandoning correct solutions

Abstract

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.