The Evolution of AI Training Paradigms: From Centralized Control to Decentralization Collaboration in a Technological Revolution

In the full value chain of AI, model training is the most resource-intensive and technically demanding stage, directly determining the upper limit of the model's capabilities and its practical application effects. Compared to the lightweight calls in the inference stage, the training process requires sustained large-scale computational power input, complex data processing workflows, and high-intensity optimization algorithm support, making it the true "heavy industry" of AI system construction. From an architectural paradigm perspective, training methods can be divided into four categories: centralized training, distributed training, federated learning, and the decentralized training that this article focuses on.

Centralized training is the most common traditional method, where a single organization completes the entire training process in a local high-performance cluster. All components, from hardware, underlying software, cluster scheduling systems, to training frameworks, are coordinated by a unified control system. This deeply collaborative architecture optimizes the efficiency of memory sharing, gradient synchronization, and fault tolerance mechanisms, making it very suitable for training large-scale models such as GPT and Gemini, with advantages of high efficiency and controllable resources. However, it also faces issues such as data monopoly, resource barriers, energy consumption, and single point risks.

Distributed training is the mainstream method for training large models. Its core is to decompose the model training tasks and distribute them to multiple machines for collaborative execution, in order to break through the bottlenecks of single-machine computation and storage. Although it physically possesses "distributed" characteristics, the overall process is still controlled, scheduled, and synchronized by centralized institutions, often operating in high-speed local area network environments, and using NVLink high-speed interconnect bus technology, with the master node coordinating all sub-tasks. Mainstream methods include:

• Data parallelism: Each node trains on different data with shared parameters, requiring matching model weights.
• Model parallelism: Deploy different parts of the model on different nodes to achieve strong scalability.
• Pipeline parallelism: Staged serial execution to improve throughput
• Tensor parallelism: fine-grained segmentation of matrix calculations to enhance parallel granularity

Distributed training is a combination of "centralized control + distributed execution", analogous to the same boss remotely directing multiple "office" employees to collaborate on tasks. Currently, almost all mainstream large models are trained in this way.

Decentralization training represents a future path with greater openness and anti-censorship characteristics. Its core features include: multiple untrusted nodes ( that may be home computers, cloud GPUs, or edge devices ) collaborating to complete training tasks without a central coordinator, usually driven by protocols for task distribution and collaboration, and leveraging cryptographic incentive mechanisms to ensure the honesty of contributions. The main challenges faced by this model include:

• Difficulty in device heterogeneity and task segmentation: High coordination difficulty for heterogeneous devices, low efficiency in task segmentation.
• Communication efficiency bottleneck: Network communication is unstable, and the gradient synchronization bottleneck is obvious.
• Lack of Trusted Execution: Lack of a trusted execution environment makes it difficult to verify whether nodes are truly participating in the computation.
• Lack of unified coordination: no central dispatcher, complex task distribution and exception rollback mechanisms

Decentralization training can be understood as: a group of global volunteers contributing computing power to collaboratively train a model, but "truly feasible large-scale decentralization training" remains a systematic engineering challenge, involving multiple aspects such as system architecture, communication protocols, cryptographic security, economic mechanisms, and model validation. However, whether it can achieve "effective collaboration + incentivize honesty + correct results" is still in the early prototyping exploration stage.

Federated learning, as a transitional form between distributed and Decentralization, emphasizes local data retention and centralized aggregation of model parameters. It is suitable for privacy-compliant scenarios such as healthcare and finance (. Federated learning possesses the engineering structure of distributed training and local collaboration capabilities, while also benefiting from the data decentralization advantages of Decentralization training. However, it still relies on trusted coordinators and does not possess completely open and censorship-resistant characteristics. It can be viewed as a "controlled Decentralization" solution in privacy-compliant scenarios, relatively mild in terms of training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture in the industry.

) AI Training Paradigm Comprehensive Comparison Table ### Technical Architecture × Trust Incentives × Application Features (

![AI Training Paradigm Evolution: From Centralized Control to Decentralization Collaborative Technological Revolution])https://img-cdn.gateio.im/webp-social/moments-f0af7b28242215cca3784f0547830879.webp(

) Decentralization training boundaries, opportunities, and realistic paths

From the perspective of training paradigms, Decentralization training is not suitable for all types of tasks. In certain scenarios, due to the complex structure of tasks, extremely high resource demands, or significant collaboration difficulties, it is inherently unsuitable for efficient completion between heterogeneous, trustless nodes. For example, large model training often relies on high memory, low latency, and high bandwidth, making it difficult to effectively partition and synchronize in open networks; tasks with strong data privacy and sovereignty constraints are limited by legal compliance and ethical restrictions, making sharing impossible; while tasks lacking a foundation for collaborative incentives lack external participation motivation. These boundaries collectively constitute the current realistic limitations of Decentralization training.

However, this does not mean that decentralized training is a false proposition. In fact, in the types of tasks that are lightweight in structure, easy to parallelize, and incentivizable, decentralized training shows clear application prospects. These include but are not limited to: LoRA fine-tuning, behavior alignment post-training tasks such as RLHF, DPO###, data crowdsourcing training and labeling tasks, resource-controllable small foundation model training, and collaborative training scenarios involving edge devices. These tasks generally possess characteristics of high parallelism, low coupling, and tolerance for heterogeneous computing power, making them very suitable for collaborative training through P2P networks, Swarm protocols, distributed optimizers, and other methods.

(# Decentralization Training Task Adaptability Overview

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( Decentralization training classic project analysis

Currently, in the forefront areas of Decentralization training and federated learning, the representative blockchain projects mainly include Prime Intellect, Pluralis.ai, Gensyn, Nous Research, and Flock.io. In terms of technological innovation and engineering implementation difficulty, Prime Intellect, Nous Research, and Pluralis.ai have made numerous original explorations in system architecture and algorithm design, representing the cutting-edge direction of current theoretical research; while Gensyn and Flock.io have relatively clear implementation paths, with preliminary engineering progress already visible. This article will sequentially analyze the core technologies and engineering architectures behind these five projects, and further discuss their differences and complementary relationships in the Decentralization AI training system.

)# Prime Intellect: A Pioneer of Verifiable Training Trajectories in Reinforcement Learning Collaborative Networks

Prime Intellect is committed to building a trustless AI training network that allows anyone to participate in training and receive credible rewards for their computational contributions. Prime Intellect aims to create a verifiable, open, and fully incentivized AI Decentralization training system through three major modules: PRIME-RL, TOPLOC, and SHARDCAST.

1. Prime Intellect Protocol Stack Structure and Key Module Value

![AI Training Paradigm Evolution: From Centralized Control to Decentralization Collaboration Technical Revolution]###https://img-cdn.gateio.im/webp-social/moments-45f26de57a53ac937af683e629dbb804.webp###

2. Detailed Explanation of the Prime Intellect Training Key Mechanism

PRIME-RL: Decoupled Asynchronous Reinforcement Learning Task Architecture

PRIME-RL is a task modeling and execution framework customized by Prime Intellect for decentralized training scenarios, specifically designed for heterogeneous networks and asynchronous participation. It employs reinforcement learning as a primary adaptation object, structurally decoupling the training, inference, and weight uploading processes, allowing each training node to independently complete task loops locally and collaborate with verification and aggregation mechanisms through standardized interfaces. Compared to traditional supervised learning processes, PRIME-RL is more suitable for implementing flexible training in environments without centralized scheduling, reducing system complexity and laying the foundation for supporting multi-task parallelism and strategy evolution.

TOPLOC: Lightweight Training Behavior Verification Mechanism

TOPLOC( Trusted Observation & Policy-Locality Check) is a core mechanism for verifiable training proposed by Prime Intellect, used to determine whether a node has genuinely completed effective policy learning based on observational data. Unlike heavy solutions like ZKML, TOPLOC does not rely on full model recomputation but instead completes lightweight structural verification by analyzing the local consistency trajectory between "observation sequence ↔ policy update." It transforms the behavioral trajectory in the training process into a verifiable object for the first time, which is a key innovation for achieving trustless training reward distribution and provides a feasible path for constructing an auditable and incentive-compatible Decentralization collaborative training network.

SHARDCAST: Asynchronous Weight Aggregation and Propagation Protocol

SHARDCAST is a weight propagation and aggregation protocol designed by Prime Intellect, optimized for real network environments that are asynchronous, bandwidth-constrained, and have constantly changing node states. It combines a gossip propagation mechanism with local synchronization strategies, allowing multiple nodes to continuously submit partial updates while in an unsynchronized state, achieving progressive convergence of weights and multi-version evolution. Compared to centralized or synchronous AllReduce methods, SHARDCAST significantly enhances the scalability and fault tolerance of Decentralization training and serves as the core foundation for building stable weight consensus and continuous training iterations.

OpenDiLoCo: Sparse Asynchronous Communication Framework

OpenDiLoCo is a communication optimization framework independently implemented and open-sourced by the Prime Intellect team based on the DiLoCo concept proposed by DeepMind, specifically designed to address common challenges in decentralized training such as bandwidth limitations, device heterogeneity, and node instability. Its architecture is based on data parallelism, building sparse topologies like Ring, Expander, and Small-World to avoid the high communication overhead of global synchronization, relying only on local neighbor nodes to accomplish collaborative model training. By combining asynchronous updates with checkpoint fault tolerance mechanisms, OpenDiLoCo enables consumer-grade GPUs and edge devices to stably participate in training tasks, significantly enhancing the accessibility of global collaborative training, and is one of the key communication infrastructures for building decentralized training networks.

PCCL: Collaborative Communication Library

PCCL(Prime Collective Communication Library) is a lightweight communication library tailored by Prime Intellect for a Decentralization AI training environment, aimed at addressing the adaptation bottlenecks of traditional communication libraries in heterogeneous devices and low-bandwidth networks. PCCL supports sparse topology, gradient compression, low-precision synchronization, and checkpoint recovery, and can run on consumer-grade GPUs and unstable nodes, serving as the underlying component supporting the asynchronous communication capabilities of the OpenDiLoCo protocol. It significantly enhances the bandwidth tolerance and device compatibility of the training network, paving the way for building a truly open, trustless collaborative training network.

3. Prime Intellect Incentive Network and Role Distribution

Prime Intellect has built a permissionless, verifiable training network with economic incentives, allowing anyone to participate in tasks and earn rewards based on real contributions. The protocol operates based on three core roles:

• Task Initiator: Define the training environment, initial model, reward function, and validation criteria.
• Training Nodes: Execute local training, submit weight updates and observation trajectories
• Validator Node: Use the TOPLOC mechanism to verify the authenticity of training behavior and participate in reward calculation and strategy aggregation.

The core process of the protocol includes task publishing, node training, trajectory verification, weight aggregation ( SHARDCAST ) and reward distribution, forming an incentive closed loop around "real training behavior".

4. INTELLECT-2: The First Verifiable Decentralization Training Model Release

Prime Intellect released INTELLECT-2 in May 2025, which is fully