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Showing posts with label Chain-of-thought reasoning. Show all posts

Thursday, January 29, 2026

The Intelligent Inflection Point: 37 Interactive Entertainment’s AI Decision System in Practice and Its Performance Breakthrough

January 29, 2026

When the “Cognitive Bottleneck” Becomes the Hidden Ceiling on Industry Growth

Over the past decade of rapid expansion in China’s gaming industry, 37 Interactive Entertainment has grown into a company with annual revenues approaching tens of billions of RMB and a complex global operating footprint. Extensive R&D pipelines, cross-market content production, and multi-language publishing have collectively pushed its requirements for information processing, creative productivity, and global response speed to unprecedented levels.

From 2020 onwards, however, structural shifts in the industry cycle became increasingly visible: user needs fragmented, regulation tightened, content competition intensified, and internal data volumes grew exponentially. Decision-making efficiency began to decline in structural ways—information fragmentation, delayed cross-team collaboration, rising costs of creative evaluation, and slower market response all started to surface. Put differently, the constraint on organizational growth was no longer “business capacity” but cognitive processing capacity.

This is the real backdrop against which 37 Interactive Entertainment entered its strategic inflection point in AI.

Problem Recognition and Internal Reflection: From Production Issues to Structural Cognitive Deficits

The earliest warning signs did not come from external shocks, but from internal research reports. These reports highlighted three categories of structural weaknesses:

Excessive decision latency: key review cycles from game green-lighting to launch were 15–30% longer than top-tier industry benchmarks.
Increasing friction in information flow: marketing, data, and R&D teams frequently suffered from “semantic misalignment,” leading to duplicated analysis and repeated creative rework.
Misalignment between creative output and global publishing: the pace of overseas localization was insufficient, constraining the window of opportunity in fast-moving overseas markets.

At root, these were not problems of effort or diligence. They reflected a deeper mismatch between the organization’s information-processing capability and the complexity of its business—a classic case of “cognitive structure ageing”.

The Turning Point and the Introduction of an AI Strategy: From Technical Pilots to Systemic Intelligent Transformation

The genuine strategic turn came after three developments:

Breakthroughs in natural language and vision models in 2022, which convinced internal teams that text and visual production were on the verge of an industry-scale transformation;
The explosive advancement of GPT-class models in 2023, which signaled a paradigm shift toward “model-first” thinking across the sector;
Intensifying competition in game exports, which made content production and publishing cadence far more time-sensitive.

Against this backdrop, 37 Interactive Entertainment formally launched its “AI Full-Chain Re-engineering Program.” The goal was not to build yet another tool, but to create an intelligent decision system spanning R&D, marketing, operations, and customer service. Notably, the first deployment scenario was not R&D, but the most standardizable use case: meeting minutes and internal knowledge capture.

The industry-specific large model “Xiao Qi” was born in this context.

Within five minutes of a meeting ending, Xiao Qi can generate high-quality minutes, automatically segment tasks based on business semantics, cluster topics, and extract risk points. As a result, meetings shift from being “information output venues” to “decision-structuring venues.” Internal feedback indicates that manual post-meeting text processing time has fallen by more than 70%.

This marked the starting point for AI’s full-scale penetration across 37 Interactive Entertainment.

Organizational Intelligent Reconfiguration: From Digital Systems to Cognitive Infrastructure

Unlike many companies that introduce AI merely as a tool, 37 Interactive Entertainment has pursued a path of systemic reconfiguration.

1. Building a Unified AI Capability Foundation

On top of existing digital systems—such as Quantum for user acquisition and Tianji for operations data—the company constructed an AI capability foundation that serves as a shared semantic and knowledge layer, connecting game development, operations, and marketing.

2. Xiao Qi as the Organization’s “Cognitive Orchestrator”

Xiao Qi currently provides more than 40 AI capabilities, covering:

Market analysis
Product ideation and green-lighting
Art production
Development assistance
Operations analytics
Advertising and user acquisition
Automated customer support
General office productivity

Each capability is more than a simple model call; it is built as a scenario-specific “cognitive chain” workflow. Users do not need to know which model is being invoked. The intelligent agent handles orchestration, verification, and model selection automatically.

3. Re-industrializing the Creative Production Chain

Within art teams, Xiao Qi does more than improve efficiency—it enables a form of creative industrialization:

Over 500,000 2D assets produced in a single quarter (an efficiency gain of more than 80%);
Over 300 3D assets, accounting for around 30% of the total;
Artists shifting from “asset producers” to curators of aesthetics and creativity.

This shift is a core marker of change in the organization’s cognitive structure.

4. Significantly Enhanced Risk Sensing and Global Coordination

AI-based translation has raised coverage of overseas game localization to more than 85%, with accuracy rates around 95%.
AI customer service has achieved an accuracy level of roughly 80%, equivalent to the output of a 30-person team.
AI-driven infringement detection has compressed response times from “by day” to “by minute,” sharply improving advertising efficiency and speeding legal response.

For the first time, the organization has acquired the capacity to understand global content risk in near real time.

Performance Outcomes: Quantifying the Cognitive Dividend

Based on publicly shared internal data and industry benchmarking, the core results of the AI strategy can be summarized as follows:

Internal documentation and meeting-related workflows are 60–80% more efficient;
R&D creative production efficiency is up by 50–80%;
AI customer service effectively replaces a 30-person team, with response speeds more than tripled;
AI translation shortens overseas launch cycles by 30–40%;
Ad creative infringement detection now operates on a minute-level cycle, cutting legal and marketing costs by roughly 20–30%.

These figures do not merely represent “automation-driven cost savings.” They are the systemic returns of an upgraded organizational cognition.

Governance and Reflection: The Art of Balance in the Age of Intelligent Systems

37 Interactive Entertainment’s internal reflection is notably sober.

1. AI Cannot Replace Value Judgement

Wang Chuanpeng frames the issue this way: “Let the thinkers make the choices, and let the dreamers create.” Even when AI can generate more options at higher quality, the questions of what to choose and why remain firmly in the realm of human creators.

2. Model Transparency and Algorithm Governance Are Non-Negotiable

The company has gradually established:

Model bias assessment protocols;
Output reliability and confidence-level checks;
AI ethics review processes;
Layered data governance and access-control frameworks.

These mechanisms are designed to ensure that “controllability” takes precedence over mere “advancement.”

3. The Industrialization Baseline Determines AI’s Upper Bound

If organizational processes, data, and standards are not sufficiently mature, AI’s value will be severely constrained. The experience at 37 Interactive Entertainment suggests a clear conclusion:
AI does not automatically create miracles; it amplifies whatever strengths and weaknesses already exist.

Appendix: Snapshot of AI Application Value

Application Scenario	AI Capabilities Used	Practical Effect	Quantitative Outcome	Strategic Significance
Meeting minutes system	NLP + semantic search	Automatically distills action items, reduces noise in discussions	Review cycles shortened by 35%	Lowers organizational decision-making friction
Infringement detection	Risk prediction + graph neural nets	Rapidly flags non-compliant creatives and alerts legal teams	Early warnings up to 2 weeks in advance	Strengthens end-to-end risk sensing
Overseas localization	Multilingual LLMs + semantic alignment	Cuts translation costs and speeds time-to-market	95% accuracy; cycles shortened by 40%	Enhances global competitiveness
Art production	Text-to-image + generative modeling	Mass generation of high-quality creative assets	Efficiency gains of around 80%	Underpins creative industrialization
Intelligent customer care	Multi-turn dialogue + intent recognition	Automatically resolves player inquiries	Output equivalent to a 30-person team	Reduces operating costs while improving experience consistency

The True Nature of the Intelligent Leap

The 37 Interactive Entertainment case highlights a frequently overlooked truth:
The revolution brought by AI is not a revolution in tools, but a revolution in cognitive structure.

In traditional organizations, information is treated primarily as a cost;
in intelligent organizations, information becomes a compressible, transformable, and reusable factor of production.

37 Interactive Entertainment’s success does not stem solely from technological leadership. It comes from upgrading its way of thinking at a critical turning point in the industry cycle—from being a mere processor of information to becoming an architect of organizational cognition.

In the competitive landscape ahead, the decisive factor will not be who has more headcount or more content, but who can build a clearer, more efficient, and more discerning “organizational brain.” AI is only the entry point. The true upper bound is set by an organization’s capacity to understand the future—and its willingness to redesign itself in light of that understanding.

Analysis of DeepSeek-R1's Product Algorithm and Implementation

January 30, 2025

Against the backdrop of rapid advancements in large models, reasoning capability has become a key metric in evaluating the quality of Large Language Models (LLMs). DeepSeek-AI recently introduced the DeepSeek-R1 series, which demonstrates outstanding reasoning capabilities. User trials indicate that its reasoning chain is richer in detail and clearer, closely aligning with user expectations. Compared to OpenAI's O1 series, DeepSeek-R1 provides a more interpretable and reliable reasoning process. This article offers an in-depth analysis of DeepSeek-R1’s product algorithm, implementation approach, and its advantages.

Core Algorithms of DeepSeek-R1

Reinforcement Learning-Driven Reasoning Optimization

DeepSeek-R1 enhances its reasoning capabilities through Reinforcement Learning (RL), incorporating two key phases:

DeepSeek-R1-Zero: Applies reinforcement learning directly to the base model without relying on Supervised Fine-Tuning (SFT). This allows the model to autonomously explore reasoning pathways, exhibiting self-verification, reflection, and long-chain reasoning capabilities.
DeepSeek-R1: Introduces Cold Start Data and a multi-stage training pipeline before RL to enhance reasoning performance, readability, and user experience.

Training Process

The training process of DeepSeek-R1 consists of the following steps:

Cold Start Data Fine-Tuning: Initial fine-tuning with a large volume of high-quality long-chain reasoning data to ensure logical clarity and readability.
Reasoning-Oriented Reinforcement Learning: RL training on specific tasks (e.g., mathematics, programming, and logical reasoning) to optimize reasoning abilities, incorporating a Language Consistency Reward to improve readability.
Rejection Sampling and Supervised Fine-Tuning: Filtering high-quality reasoning pathways generated by the RL model for further fine-tuning, enhancing general abilities in writing, Q&A, and other applications.
Reinforcement Learning for All Scenarios: Integrating multiple reward signals to balance reasoning performance, helpfulness, and harmlessness.
Knowledge Distillation: Transferring DeepSeek-R1’s reasoning capability to smaller models to improve efficiency and reduce computational costs.

Comparison Between DeepSeek-R1 and OpenAI O1

Logical Reasoning Capability

Experimental results indicate that DeepSeek-R1 performs on par with or even surpasses OpenAI O1-1217 in mathematics, coding, and logical reasoning. For example, in the AIME 2024 mathematics competition, DeepSeek-R1 achieved a Pass@1 score of 79.8%, slightly higher than O1-1217’s 79.2%.

Interpretability and Readability

DeepSeek-R1’s reasoning process is more detailed and readable due to:

The use of explicit reasoning format tags such as <think> and <answer>.
The introduction of a language consistency reward during training, reducing language-mixing issues.
Cold start data ensuring initial stability in the RL phase.

In contrast, while OpenAI’s O1 series generates longer reasoning chains, some responses lack clarity, making them harder to comprehend. DeepSeek-R1’s optimizations improve interpretability, making it easier for users to understand the reasoning process.

Reliability of Results

DeepSeek-R1 employs a self-verification mechanism, allowing the model to actively reflect on and correct errors during reasoning. Experiments demonstrate that this mechanism effectively reduces logical inconsistencies and enhances the coherence of the reasoning process. By comparison, OpenAI O1 occasionally produces plausible yet misleading answers without deep logical validation.

Conclusion

DeepSeek-R1 excels in reasoning capability, interpretability, and reliability. By combining reinforcement learning with cold start data, the model provides a more detailed analysis, making its working principles more comprehensible. Compared to OpenAI's O1 series, DeepSeek-R1 has clear advantages in interpretability and consistency, making it particularly suitable for applications requiring structured reasoning, such as mathematical problem-solving, coding tasks, and complex decision support.

Moving forward, DeepSeek-AI may further refine the model’s general capabilities, enhance multilingual reasoning support, and expand its applications in software engineering, knowledge management, and other domains.

Join the HaxiTAG Community to engage in discussions and share datasets for Chain-of-Thought (CoT) training. Collaborate with experts, exchange best practices, and enhance reasoning model performance through community-driven insights and knowledge sharing.

Learning to Reason with LLMs: A Comprehensive Analysis of OpenAI o1

September 15, 2024

This document provides an in-depth analysis of OpenAI o1, a large language model (LLM) that leverages reinforcement learning and chain-of-thought reasoning to achieve significant advancements in complex reasoning tasks.

Core Insights and Problem Solving

Major Insights:

Chain-of-thought reasoning significantly improves LLM performance on complex tasks. o1 demonstrates that by mimicking human-like thought processes, LLMs can achieve higher accuracy in problem-solving across various domains like coding, mathematics, and science.

Reinforcement learning is an effective method for training LLMs to reason productively. OpenAI's data-efficient algorithm leverages chain-of-thought within a reinforcement learning framework, allowing the model to learn from its mistakes and refine its problem-solving strategies.

Performance scales with both train-time compute (reinforcement learning) and test-time compute (thinking time). This suggests that further improvements can be achieved through increased computational resources and allowing the model more time to reason.

Chain-of-thought offers potential for enhanced safety and alignment. Observing the model's reasoning process enables better understanding and control, allowing for more effective integration of safety policies.

Key Problems Solved:

Limited reasoning capabilities of previous LLMs: o1 surpasses previous models like GPT-4o in its ability to tackle complex, multi-step problems requiring logical deduction and problem-solving.

Difficulties in evaluating LLM reasoning: The introduction of chain-of-thought provides a more transparent and interpretable framework for evaluating the reasoning process of LLMs.

Challenges in aligning LLMs with human values: Chain-of-thought enables the integration of safety policies within the reasoning process, leading to more robust and reliable adherence to ethical guidelines.

Specific Solutions:

Chain-of-thought reasoning: Training the model to generate an internal sequence of thought steps before producing an answer.

Reinforcement learning with chain-of-thought: Utilizing a data-efficient reinforcement learning algorithm to refine the model's ability to utilize chain-of-thought effectively.

Test-time selection strategies: Employing methods to select the best candidate submissions based on performance on various test cases and learned scoring functions.

Hiding raw chain-of-thought from users: Presenting a summarized version of the reasoning process to maintain user experience and competitive advantage while potentially enabling future monitoring capabilities. (via here)

Solution Details

Chain-of-Thought Reasoning:

Prompting: The model is provided with a problem that requires reasoning.

Internal Reasoning: The model generates a sequence of intermediate thought steps that lead to the final answer. This chain-of-thought mimics the way humans might approach the problem.

Answer Generation: Based on the chain-of-thought, the model produces the final answer.

Reinforcement Learning with Chain-of-Thought:

Initial Training: The model is pre-trained on a large dataset of text and code.

Chain-of-Thought Generation: The model is prompted to generate chains-of-thought for reasoning problems.

Reward Signal: A reward function evaluates the quality of the generated chain-of-thought and the final answer.

Policy Optimization: The model's parameters are updated based on the reward signal to improve its ability to generate effective chains-of-thought.

Practice Guide:

Understanding the basics of LLMs and reinforcement learning is crucial.

Experiment with different prompting techniques to elicit chain-of-thought reasoning.

Carefully design the reward function to encourage productive reasoning steps.

Monitor the model's chain-of-thought during training to identify and address any biases or errors.

Consider the ethical implications of using chain-of-thought and ensure responsible deployment.

Experience and Considerations:

Chain-of-thought can be computationally expensive, especially for complex problems.

The effectiveness of chain-of-thought depends on the quality of the pre-training data and the reward function.

It is essential to address potential biases and ensure fairness in the training data and reward function.

Carefully evaluate the model's performance and potential risks before deploying it in real-world applications.

Main Content Summary

Core Argument: Chain-of-thought reasoning, combined with reinforcement learning, significantly improves the ability of LLMs to perform complex reasoning tasks.

Limitations and Constraints:

Computational cost: Chain-of-thought can be resource-intensive.

Dependence on pre-training data and reward function: The effectiveness of the method relies heavily on the quality of the training data and the design of the reward function.

Potential biases: Biases in the training data can be reflected in the model's reasoning process.

Limited applicability: While o1 excels in reasoning-heavy domains, it may not be suitable for all natural language processing tasks.

Product, Technology, and Business Introduction

OpenAI o1: A new large language model trained with reinforcement learning and chain-of-thought reasoning to enhance complex problem-solving abilities.

Key Features:

Improved Reasoning: o1 demonstrates significantly better performance in reasoning tasks compared to previous models like GPT-4o.

Chain-of-Thought: Mimics human-like reasoning by generating intermediate thought steps before producing an answer.

Reinforcement Learning: Trained using a data-efficient reinforcement learning algorithm that leverages chain-of-thought.

Scalable Performance: Performance improves with increased train-time and test-time compute.

Enhanced Safety and Alignment: Chain-of-thought enables better integration of safety policies and monitoring capabilities.

Target Applications:

Coding: Competitive programming, code generation, debugging.

Mathematics: Solving complex mathematical problems, automated theorem proving.

Science: Scientific discovery, data analysis, problem-solving in various scientific domains.

Education: Personalized tutoring, automated grading, educational content generation.

Research: Advancing the field of artificial intelligence and natural language processing.

GPT-4o1 Model Analysis

How does large-scale reinforcement learning enhance reasoning ability?

Reinforcement learning allows the model to learn from its successes and failures in generating chains-of-thought. By receiving feedback in the form of rewards, the model iteratively improves its ability to generate productive reasoning steps, leading to better problem-solving outcomes.

Chain-of-Thought Training Implementation:

Dataset Creation: A dataset of reasoning problems with corresponding human-generated chains-of-thought is created.

Model Fine-tuning: The LLM is fine-tuned on this dataset, learning to generate chains-of-thought based on the input problem.

Reinforcement Learning: The model is trained using reinforcement learning, where it receives rewards for generating chains-of-thought that lead to correct answers. The reward function guides the model towards developing effective reasoning strategies.

Learning from Errors:

The reinforcement learning process allows the model to learn from its mistakes. When the model generates an incorrect answer or an ineffective chain-of-thought, it receives a negative reward. This feedback signal helps the model adjust its parameters and improve its reasoning abilities over time.

Model Upgrade Process

GPT-4o's Main Problems:

Limited reasoning capabilities compared to humans in complex tasks.

Lack of transparency in the reasoning process.

Challenges in aligning the model with human values and safety guidelines.

GPT-4o1 Development Motives and Goals:

Improve reasoning abilities to achieve human-level performance on challenging tasks.

Enhance transparency and interpretability of the reasoning process.

Strengthen safety and alignment mechanisms to ensure responsible AI development.

Solved Problems and Achieved Results:

Improved Reasoning: o1 significantly outperforms GPT-4o on various reasoning benchmarks, including competitive programming, mathematics, and science problems.

Enhanced Transparency: Chain-of-thought provides a more legible and interpretable representation of the model's reasoning process.

Increased Safety: o1 demonstrates improved performance on safety evaluations and reduced vulnerability to jailbreak attempts.

Implementation Methods and Steps:

Chain-of-Thought Integration: Implementing chain-of-thought reasoning within the model's architecture.

Reinforcement Learning with Chain-of-Thought: Training the model using a data-efficient reinforcement learning algorithm that leverages chain-of-thought.

Test-Time Selection Strategies: Developing methods for selecting the best candidate submissions during evaluation.

Safety and Alignment Enhancements: Integrating safety policies and red-teaming to ensure responsible model behavior.

Verification and Reasoning Methods

Simulated Path Verification:

This involves generating multiple chain-of-thought paths for a given problem and selecting the path that leads to the most consistent and plausible answer. By exploring different reasoning avenues, the model can reduce the risk of errors due to biases or incomplete information.

Logic-Based Reliable Pattern Usage:

The model learns to identify and apply reliable logical patterns during its reasoning process. This involves recognizing common problem-solving strategies, applying deductive reasoning, and verifying the validity of intermediate steps.

Combined Approach:

These two methods work in tandem. Simulated path verification explores multiple reasoning possibilities, while logic-based pattern usage ensures that each path follows sound logical principles. This combined approach helps the model arrive at more accurate and reliable conclusions.

GPT-4o1 Optimization Mechanisms

Feedback Optimization Implementation:

Human Feedback: Human evaluators provide feedback on the quality of the model's responses, including the clarity and logic of its chain-of-thought.

Reward Signal Generation: Based on human feedback, a reward signal is generated to guide the model's learning process.

Reinforcement Learning Fine-tuning: The model is fine-tuned using reinforcement learning, where it receives rewards for generating responses that align with human preferences.

LLM-Based Logic Rule Acquisition:

The LLM can learn logical rules and inference patterns from the vast amount of text and code it is trained on. By analyzing the relationships between different concepts and statements in the training data, the model can extract general logical principles that it can apply during reasoning tasks. For example, the model can learn that "if A implies B, and B implies C, then A implies C."

Domain-Specific Capability Enhancement Methodology

Enhancing Domain-Specific Abilities in LLMs via Reinforcement Learning:

1. Thinking Process and Validation:

Identify the target domain: Clearly define the specific area where you want to improve the LLM's capabilities (e.g., medical diagnosis, legal reasoning, financial analysis).

Analyze expert reasoning: Study how human experts in the target domain approach problems, including their thought processes, strategies, and knowledge base.

Develop domain-specific benchmarks: Create evaluation datasets that accurately measure the LLM's performance in the target domain.

2. Algorithm Design:

Pre-training with domain-specific data: Fine-tune the LLM on a large corpus of text and code relevant to the target domain.

Reinforcement learning framework: Design a reinforcement learning environment where the LLM interacts with problems in the target domain and receives rewards for generating correct solutions and logical chains-of-thought.

Reward function design: Carefully craft a reward function that incentivizes the LLM to acquire domain-specific knowledge, apply relevant reasoning strategies, and produce accurate outputs.

3. Training Analysis and Data Validation:

Iterative training: Train the LLM using the reinforcement learning framework, monitoring its progress on the domain-specific benchmarks.

Error analysis: Analyze the LLM's errors and identify areas where it struggles in the target domain.

Data augmentation: Supplement the training data with additional examples or synthetic data to address identified weaknesses.

4. Expected Outcomes and Domain Constraint Research:

Evaluation on benchmarks: Evaluate the LLM's performance on the domain-specific benchmarks and compare it to human expert performance.

Qualitative analysis: Analyze the LLM's generated chains-of-thought to understand its reasoning process and identify any biases or limitations.

Domain constraint identification: Research and document the limitations and constraints of the LLM in the target domain, including its ability to handle edge cases and out-of-distribution scenarios.

Expected Results:

Improved accuracy and efficiency in solving problems in the target domain.

Enhanced ability to generate logical and insightful chains-of-thought.

Increased reliability and trustworthiness in domain-specific applications.

Domain Constraints:

The effectiveness of the methodology will depend on the availability of high-quality domain-specific data and the complexity of the target domain.

LLMs may still struggle with tasks that require common sense reasoning or nuanced understanding of human behavior within the target domain.

Ethical considerations and potential biases should be carefully addressed during data collection, model training, and deployment.

This methodology provides a roadmap for leveraging reinforcement learning to enhance the domain-specific capabilities of LLMs, opening up new possibilities for AI applications across various fields.

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Thursday, January 29, 2026

When the “Cognitive Bottleneck” Becomes the Hidden Ceiling on Industry Growth

Problem Recognition and Internal Reflection: From Production Issues to Structural Cognitive Deficits

The Turning Point and the Introduction of an AI Strategy: From Technical Pilots to Systemic Intelligent Transformation

Organizational Intelligent Reconfiguration: From Digital Systems to Cognitive Infrastructure

1. Building a Unified AI Capability Foundation

2. Xiao Qi as the Organization’s “Cognitive Orchestrator”

3. Re-industrializing the Creative Production Chain

4. Significantly Enhanced Risk Sensing and Global Coordination

Performance Outcomes: Quantifying the Cognitive Dividend

Governance and Reflection: The Art of Balance in the Age of Intelligent Systems

1. AI Cannot Replace Value Judgement

2. Model Transparency and Algorithm Governance Are Non-Negotiable

3. The Industrialization Baseline Determines AI’s Upper Bound

Appendix: Snapshot of AI Application Value

The True Nature of the Intelligent Leap

Related Topic

Thursday, January 30, 2025

Core Algorithms of DeepSeek-R1

Reinforcement Learning-Driven Reasoning Optimization

Training Process

Comparison Between DeepSeek-R1 and OpenAI O1

Logical Reasoning Capability

Interpretability and Readability

Reliability of Results

Conclusion

Related Topic

Sunday, September 15, 2024

Core Insights and Problem Solving

Major Insights:

Key Problems Solved:

Specific Solutions:

Solution Details

Chain-of-Thought Reasoning:

Practice Guide:

Experience and Considerations:

Main Content Summary

Key Features:

Target Applications:

GPT-4o1 Model Analysis

Chain-of-Thought Training Implementation:

Model Upgrade Process

GPT-4o's Main Problems:

Implementation Methods and Steps:

Verification and Reasoning Methods

Simulated Path Verification:

Logic-Based Reliable Pattern Usage:

Combined Approach:

GPT-4o1 Optimization Mechanisms

Feedback Optimization Implementation:

LLM-Based Logic Rule Acquisition:

Domain-Specific Capability Enhancement Methodology

Enhancing Domain-Specific Abilities in LLMs via Reinforcement Learning:

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