CAPO Framework: How AI Learns Like Humans from Imitation to Discrimination
13 days ago 高效码农From Imitation to Discrimination: How a Generalized Curriculum Advantage Mechanism Enhances Cross-Domain Reasoning in AI Summary: This article introduces CAPO (Curriculum Advantage Policy Optimization), an innovative reinforcement learning training paradigm. It employs a staged curriculum, first using positive-advantage samples for imitation learning to build a stable foundation, then introducing negative-advantage samples for discrimination learning to enhance generalization. The method is compatible with mainstream optimization algorithms like GRPO and PPO, consistently improving mathematical reasoning performance by 1.7 to 4.0 points, and effectively generalizes to multimodal GUI reasoning scenarios with a 3.81-point gain, establishing itself as a versatile and robust optimization framework. …
DeepSeek-V3.2: The Open-Source LLM Challenging GPT-5 & Gemini-3.0 in AI Reasoning
21 days ago 高效码农DeepSeek-V3.2: Pushing the Frontier of Open-Source Large Language Models In today’s rapidly evolving artificial intelligence landscape, large language models (LLMs) have become the core driving force behind technological advancement. Recently, DeepSeek-AI released the全新的DeepSeek-V3.2 model, a breakthrough that not only delivers outstanding performance across multiple benchmarks but also achieves an ingenious balance between efficiency and capability, injecting new vitality into the open-source AI community. Model Overview: The Perfect Fusion of Efficient Reasoning and Agentic AI DeepSeek-V3.2 is a large language model that integrates efficient computation, exceptional reasoning ability, and agent performance. It’s built upon three key technological innovations: DeepSeek Sparse Attention …
Qwen3-Next-80B-A3B-Thinking: The Ultimate Guide to AI’s Most Advanced Reasoning Model
23 days ago 高效码农A Comprehensive Guide to Qwen3-Next-80B-A3B-Thinking: Technical Breakthroughs and Practical Applications In the rapidly evolving field of artificial intelligence, large language models are advancing toward larger parameter scales and stronger contextual processing capabilities. The model we’re exploring today—Qwen3-Next-80B-A3B-Thinking—represents a significant achievement in this trend. Whether you’re an AI developer, researcher, or someone interested in cutting-edge technology, this article will provide a thorough analysis of this model’s technical characteristics, performance, and practical application methods. What is Qwen3-Next-80B-A3B-Thinking? Qwen3-Next-80B-A3B-Thinking is the first version in the Qwen team’s new generation of foundation model series. This model is specifically optimized for complex reasoning tasks, achieving …
How Reinforcement Learning Transforms Large Language Models into Powerful Reasoning Engines
27 days ago 高效码农Enhancing Reasoning Capabilities in Large Language Models Through Reinforcement Learning In the rapidly evolving field of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities across various domains. However, one persistent challenge has been equipping these models with deeper reasoning abilities. Recent research reveals that reinforcement learning (RL) techniques can significantly enhance language models’ performance on complex tasks requiring logical thinking and multi-step problem-solving. This article explores the latest advancements in this field, particularly how innovative training methodologies can help models maintain their broad knowledge while developing stronger analytical capabilities. Why Reinforcement Learning is Necessary for Advanced Language Models …
SofT-GRPO: How Gumbel-Softmax Revolutionizes LLM Reinforcement Learning
1 months ago 高效码农SofT-GRPO: Revolutionizing LLM Reinforcement Learning with Soft-Thinking Policy Optimization Core Question Answered This article explains how SofT-GRPO solves the fundamental challenge of applying reinforcement learning to soft-thinking LLMs, achieving superior performance over discrete-token methods through innovative Gumbel noise injection and reparameterization techniques. Introduction: The Bottleneck of Traditional Discrete-Token Reasoning Large language models have transformed reasoning capabilities across diverse domains, yet most existing methods remain constrained by discrete token selection. This limitation manifests in two critical ways: first, it restricts the model’s ability to represent abstract concepts that cannot be easily captured by single tokens; second, it forces sequential reasoning that …
TraceRL Revolutionizes Reinforcement Learning for Diffusion Language Models in Complex Reasoning
2 months ago 高效码农Revolutionizing Reinforcement Learning for Diffusion Language Models How can we make diffusion language models excel at complex reasoning tasks like mathematics and coding? The answer lies in a groundbreaking trajectory-aware reinforcement learning framework called TraceRL, which aligns training objectives with the model’s actual inference process. Diffusion language models (DLMs) represent a paradigm shift in language generation, offering parallel decoding capabilities and bidirectional attention mechanisms. However, their full potential has been limited by a fundamental mismatch between traditional training objectives and the actual inference trajectory. This article introduces TraceRL—a revolutionary reinforcement learning framework that addresses this core limitation and enables DLMs …
MetaStone-S1: How 32B Beats OpenAI o3-mini with Draft Paper Strategy
4 months ago 高效码农From Quick Guesses to Thoughtful Drafts: How MetaStone-S1 Makes a 32 B Model Rival OpenAI o3-mini 1. Why Do Large Language Models Need Draft Paper? Imagine you are taking a tough math final. If you must write the final answer in one shot, you will probably lose points. Give yourself scratch paper, let yourself jot down three different approaches, and then hand in the cleanest version—your score jumps. Large language models (LLMs) face the same problem. Traditional models generate one answer and stop. A newer idea called Test-Time Scaling (TTS) lets the model create many “draft solutions” at inference time, …
Gemini Deep Think: How Google’s AI Solves Complex Problems Like Humans
4 months ago 高效码农Gemini 2.5 Deep Think: When AI Takes the Time to Truly Think Gemini 2.5 Deep Think now available for Ultra subscribers! Great at tackling problems that require creativity & planning, it finds the best answer by considering, revising & combining many ideas at once. A faster variation of the model that just achieved IMO gold-level. Enjoy! Have you ever wished your AI assistant could take a moment to really think through complex problems before responding? Not just give you the first answer that comes to mind, but actually explore different angles, weigh potential solutions, and refine its thinking—much like how …
Interleaved Reasoning Technology: Revolutionizing AI’s Thought Process for Smarter Decisions
6 months ago 高效码农How to Make Large Language Models Reason More Intelligently? An In-Depth Exploration of Interleaved Reasoning Technology In today’s digital age, with the continuous development of artificial intelligence technology, large language models (LLMs) have become an extremely powerful tool, playing a significant role in numerous fields. However, despite their excellent performance in text generation, these models still have limitations when it comes to handling complex reasoning tasks. Today, let’s delve into a technology that can significantly enhance the reasoning capabilities of large language models—interleaved reasoning, and see how it changes the game. I. The Current Status and Challenges of Reasoning with …
DeepSeek-R1-0528: Revolutionizing AI Reasoning Capabilities with Advanced Problem-Solving
6 months ago 高效码农DeepSeek-R1-0528: Revolutionizing Reasoning Capabilities in Large Language Models Discover how DeepSeek’s latest upgrade transforms AI problem-solving with unprecedented reasoning depth and practical usability. 🔍 Key Breakthroughs in Reasoning Capabilities DeepSeek-R1-0528 represents a quantum leap in AI reasoning, achieved through algorithmic refinements and enhanced computational scaling: • 87.5% accuracy on AIME 2025 advanced math problems (vs. 70% in prior version) • 92% deeper reasoning chains: Average token usage per complex problem surged from 12K → 23K • Hallucination reduction and enhanced tool-calling support Performance Comparison Capability Use Case Improvement Mathematical Reasoning AIME/HMMT contests +17%–38% Code Generation Codeforces/SWE tasks +24%–37% Tool Integration …
Chain-of-Recursive-Thoughts (CoRT): How Self-Debate Makes AI Smarter Through Iterative Learning
7 months ago 高效码农How Chain-of-Recursive-Thoughts (CoRT) Makes AI Smarter Through Self-Debate Why Current AI Needs a Critical Thinking Upgrade Even state-of-the-art AI models occasionally produce puzzling outputs – like a math professor failing basic arithmetic. This gap between potential and performance inspired Chain-of-Recursive-Thoughts (CoRT), a groundbreaking method that teaches AI to systematically refine its answers through self-evaluation. Traditional AI operates like an overconfident student: answer first, think never. CoRT transforms this process into an expert peer-review system, achieving measurable improvements in programming assistance, logical reasoning, and technical analysis. Understanding the CoRT Framework The Self-Improvement Loop CoRT enables AI to: Generate multiple solution candidates …
Xiaomi MiMo-7B: The Compact AI Powerhouse Redefining Reasoning Efficiency
7 months ago 高效码农Xiaomi MiMo-7B: Small Model, Big Intelligence – Redefining AI Reasoning Capabilities Xiaomi-MiMo Introduction: The Rise of Compact Powerhouses in AI The AI industry has long operated under the assumption that bigger models mean better performance. Yet Xiaomi’s MiMo-7B series shatters this myth completely. With just 7 billion parameters, these open-source models outperform multiple 32B-scale competitors in mathematical reasoning and code generation tasks, even rivaling OpenAI’s o1-mini. What makes this breakthrough truly revolutionary? Xiaomi has open-sourced the complete training framework, model weights, and technical blueprints – a gift to developers worldwide seeking efficient reasoning-focused AI solutions. Technical Breakthroughs: How a 7B …