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

In the entire value chain of AI, model training is the most resource-intensive and technically challenging phase, directly determining the upper limit of the model's capabilities and its practical application effectiveness. Compared to the lightweight calls in the inference phase, the training process requires continuous large-scale computing power investment, 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 Decentralization training.

Centralized training is the most common traditional method, where a single institution completes the entire training process within a local high-performance cluster, with all components from hardware, underlying software, cluster scheduling systems, to training frameworks 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 like 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 today. 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, it is still controlled, scheduled, and synchronized by centralized institutions, often operating in high-speed local area network environments, using NVLink high-speed interconnect bus technology, with the main node coordinating all sub-tasks. Mainstream methods include:

• Data parallelism: Each node trains on different data while sharing model weights, requiring matching of model weights.
• Model parallelism: Deploy different parts of the model on different nodes to achieve strong scalability.
• Pipeline Parallelism: Staged Serial Execution, Improving Throughput
• Tensor Parallelism: Fine-grained partitioning of matrix computations 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 completing tasks. Currently, almost all mainstream large models are trained using this method.

Decentralization training represents a future path that is more open and resistant to censorship. Its core features include: multiple untrusted nodes ( which can be personal computers, cloud GPUs, or edge devices ) collaborating to complete training tasks without a central coordinator, usually driven by protocols for task distribution and cooperation, and using cryptographic incentive mechanisms to ensure the honesty of contributions. The main challenges faced by this model include:

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

Decentralization training can be understood as: a group of global volunteers contributing computing power to collaboratively train models. However, "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. Whether it can achieve "collaborative effectiveness + incentive honesty + correct results" is still in the early prototype exploration stage.

Federated learning, as a transitional form between distributed and Decentralization, emphasizes local data retention and centralized aggregation of model parameters, making it suitable for privacy-compliant scenarios such as healthcare and finance (. Federated learning has the engineering structure of distributed training and local collaboration capabilities, while also possessing the data dispersion advantages of Decentralization training, but it still relies on trusted coordinating parties and does not have fully open and censorship-resistant characteristics. It can be seen as a "controlled Decentralization" solution in privacy-compliant scenarios, with relatively mild training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture for the industry.

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

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) Decentralization training's 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 complexity of task structures, extremely high resource requirements, or the difficulty of collaboration, it is inherently unsuitable for efficient completion among 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 an open network; tasks with strong data privacy and sovereignty restrictions, such as ### in healthcare, finance, and sensitive data (, are constrained by legal compliance and ethical constraints, preventing open sharing; and tasks lacking a foundation for collaborative incentives, such as enterprise closed-source models or internal prototype training ), lack external participation motivation. These boundaries collectively constitute the current practical limitations of Decentralization training.

However, this does not mean that decentralized training is a false proposition. In fact, in lightweight, easily parallelizable, and incentivized task types, decentralized training shows clear application prospects. This includes but is not limited to: LoRA fine-tuning, behavior alignment post-training tasks ( such as RLHF, DPO ), data crowdsourcing training and annotation tasks, resource-controllable small foundational model training, and collaborative training scenarios involving edge devices. These tasks generally possess 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 Table

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

( Decentralization training classic project analysis

Currently, in the forefront of decentralized training and federated learning, 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 proposed many 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, and preliminary engineering progress can already be seen. This article will sequentially analyze the core technologies and engineering architecture behind these five projects and further explore their differences and complementary relationships in the decentralized AI training system.

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

Prime Intellect is dedicated 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 the 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 Collaborative Technological Revolution]###https://img-cdn.gateio.im/webp-social/moments-3a83d085e7a7abfe72221958419cd6d8.webp###

2. Detailed Explanation of Prime Intellect Training Key Mechanisms

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, designed specifically for heterogeneous networks and asynchronous participation. It utilizes reinforcement learning as the primary adaptation object, structurally decoupling the training, inference, and weight upload processes, allowing each training node to independently complete task loops locally and collaborate through standardized interfaces with validation and aggregation mechanisms. 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 policy evolution.

TOPLOC: Lightweight Training Behavior Verification Mechanism

TOPLOC(Trusted Observation & Policy-Locality Check) is a core mechanism proposed by Prime Intellect for verifying the training process, used to determine whether a node has truly completed effective policy learning based on observational data. Unlike heavy solutions such as 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 trajectories during the training process into verifiable objects for the first time, representing a key innovation for achieving trustless training reward distribution, and provides a feasible path for building an auditable and incentivized 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 variable node states. It combines a gossip propagation mechanism with local synchronization strategies, allowing multiple nodes to continuously submit partial updates in asynchronous states, 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, serving as a 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. It is designed specifically to address challenges such as bandwidth limitations, device heterogeneity, and node instability commonly encountered in Decentralization training. Its architecture is based on data parallelism, constructing sparse topological structures such as Ring, Expander, and Small-World, which avoids the high communication overhead of global synchronization and allows model collaborative training to be completed by relying only on local neighbor nodes. By combining asynchronous updates and fault tolerance mechanisms, OpenDiLoCo enables consumer-grade GPUs and edge devices to stably participate in training tasks, significantly enhancing the participability of global collaborative training, making it one of the key communication infrastructures for building Decentralization training networks.

PCCL: Collaborative Communication Library

PCCL(Prime Collective Communication Library) is a lightweight communication library tailored for the decentralized AI training environment by Prime Intellect, aimed at solving 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 and trustless collaborative training network by addressing the "last mile" of communication infrastructure.

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 Node: 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 processes of the protocol include task publishing, node training, trajectory verification, weight aggregation ( SHARDCAST ), and reward distribution, forming an incentive closed loop around "real training behavior".

IV. INTELLECT-2: The Release of the First Verifiable Decentralization Training Model

Prime Intellect launched INTELLECT-2 in May 2025, which is the world's first large reinforcement learning model trained through asynchronous and trustless Decentralization node collaboration, with a parameter scale of 32B. The INTELLECT-2 model is composed of extensive