K2-Think: How a 32-Billion-Parameter Model Outperforms Giants in Math Olympiads
1 months ago 高效码农A conversation starter “Can a model small enough to fit on four gaming GPUs beat the latest 120-billion-parameter heavyweights at high-school math competitions?” The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) just proved the answer is ‘yes’. Below is a fully-transparent walk-through of their K2-Think recipe—data, code, training budget, safety filters and all—rewritten for junior-college graduates and busy engineers who simply want facts, numbers and reproducible steps. 1. Thirty-second summary Base model: Qwen2.5-32B (completely open weights) Post-training data: one open-source set, 92 k problems with automatically checkable answers Training stages: long-chain supervised fine-tuning → verifiable-reward RL → simple test-time …
Revolutionizing AI Reasoning: How HRM Achieves Superior Efficiency and Accuracy
3 months ago 高效码农Revolutionary AI Model HRM: Solving Complex Reasoning Challenges Understanding Hierarchical Reasoning Models (HRM) Artificial Intelligence has taken a significant leap with the introduction of the Hierarchical Reasoning Model (HRM). This breakthrough architecture, developed by Guan Wang’s team at Tsinghua University, addresses long-standing limitations in large language models’ reasoning capabilities. Unlike traditional Chain-of-Thought (CoT) approaches that require millions of training samples and generate excessive computational overhead, HRM achieves remarkable efficiency with just 27 million parameters and 1,000 training examples . Why Traditional Approaches Fall Short Current AI reasoning methods face critical challenges: Excessive Data Requirements: Most models need millions of training …