Reinforcement Learning Reshapes Decentralized AI: From Computing Power Networks to Intelligent Evolution

The current development of AI is at a critical inflection point. Large models have shifted from mere “pattern fitting” toward “structured reasoning,” and the core driver of this transformation is reinforcement learning technology. The emergence of DeepSeek-R1 marks the maturation of this shift—reinforcement learning is no longer just a fine-tuning tool but has become the primary technical pathway for system-level reasoning enhancement. Meanwhile, Web3, through decentralized compute networks and cryptographic incentive systems, is reconstructing the AI production paradigm. The collision of these forces has produced unexpected chemical reactions: reinforcement learning’s demands for distributed sampling, reward signals, and verifiable training align naturally with blockchain’s decentralized collaboration, incentive distribution, and auditable execution.

This article will start from the technical principles of reinforcement learning, revealing its deep logical complementarity with Web3 structures. Through practical cases from frontier projects like Prime Intellect, Gensyn, and Nous Research, it will demonstrate the feasibility and prospects of decentralized reinforcement learning networks.

The Three-Layer Architecture of Reinforcement Learning: From Theory to Application

Theoretical Foundation: How Reinforcement Learning Drives AI Evolution

Reinforcement learning (RL) is fundamentally a “trial-and-error optimization” paradigm. Through a closed loop of “interacting with the environment → receiving rewards → adjusting strategies,” the model becomes smarter with each iteration. This is starkly different from traditional supervised learning, which relies on labeled data—RL enables AI to learn to improve autonomously from experience.

A complete RL system involves three core roles:

• Policy Network: The decision-making brain, generating actions based on environment states
• Experience Sampling (Rollout): The executor interacting with the environment to generate training data
• Learner: Processes all sampled data, computes gradients, and updates the policy

The most critical insight is: Sampling can be fully parallelized, while parameter updates require centralized synchronization. This characteristic opens the door for decentralized training.

Modern LLM Training Panorama: A Three-Stage Framework

Today’s large language model training can be divided into three progressive stages, each with distinct missions:

Pre-training — Building the World Model
Self-supervised learning on trillions of tokens establishes the model’s general capabilities. This stage requires thousands of centralized GPUs, with enormous communication overhead—accounting for 80-95% of costs—and inherently depends on highly centralized cloud providers.

Fine-tuning — Injecting Task Capabilities
Using smaller datasets to inject specific task abilities, accounting for 5-15% of costs. While supporting distributed execution, gradient synchronization still requires centralized coordination, limiting decentralization potential.

Post-training — Shaping Reasoning and Values
This is where reinforcement learning plays a role. Methods include RLHF (Reinforcement Learning from Human Feedback), RLAIF (AI Feedback Reinforcement Learning), GRPO (Group Relative Policy Optimization), among others. Cost accounts for only 5-10%, but can significantly improve reasoning ability, safety, and alignment. The key advantage is that this stage naturally supports asynchronous distributed execution: nodes do not need to hold full weights, and combining verifiable computation with on-chain incentives can form an open decentralized training network.

Why is post-training most suitable for Web3? Because RL’s demand for sampling (rollouts) is “infinite”—generating more reasoning trajectories always makes the model smarter. Sampling tasks are also the easiest to distribute globally and require minimal inter-node communication.

The Evolution of Reinforcement Learning Technology: From RLHF to GRPO

The Five-Stage Reinforcement Learning Process

Stage 1: Data Generation (Policy Exploration)
The policy model generates multiple reasoning chains given prompts, providing samples for preference evaluation. The breadth of this step determines the richness of exploration.

Stage 2: Preference Feedback (RLHF / RLAIF)

• RLHF: Human annotators compare model outputs, selecting better answers. This was key in upgrading GPT-3.5 to GPT-4 but is costly and hard to scale.
• RLAIF: Replaces human annotation with AI reviewers or predefined rules, enabling automation and scaling. Projects like OpenAI, Anthropic, and DeepSeek have adopted this paradigm.

Stage 3: Reward Modeling (Reward Modeling)

• RM (Reward Model): Evaluates the quality of the final answer, assigning a score.
• PRM (Process Reward Model): A key innovation in OpenAI’s o1 and DeepSeek-R1, which scores each step, token, and logical segment of the reasoning chain—essentially teaching the model how to think correctly.

Stage 4: Reward Verifiability
In distributed environments, reward signals must come from reproducible rules, facts, or consensus. Zero-knowledge proofs (ZK) and proof of learnability (PoL) provide cryptographic guarantees, ensuring rewards are tamper-proof and auditable.

Stage 5: Policy Optimization
Updating model parameters guided by reward signals. The most debated methodology here includes:

• PPO: The traditional approach, stable but slow to converge.
• GRPO: DeepSeek-R1’s core innovation, modeling relative advantage within groups rather than simple ranking, better suited for reasoning tasks, leading to more stable training.
• DPO: Does not generate trajectories or build reward models, directly optimizing on preference pairs; low cost but limited in improving reasoning capabilities.

The Natural Complementarity of Reinforcement Learning and Web3

Separation of Reasoning and Training

The RL training process can be explicitly split:

• Rollout (Sampling): Generating large amounts of data, compute-intensive but with sparse communication, suitable for parallel execution on consumer-grade GPUs.
• Update (Gradient Computation): Requires high-bandwidth, centralized parameter synchronization.

This aligns perfectly with Web3’s decentralized network architecture: outsourcing sampling to global GPU resources with contribution-based token rewards; keeping parameter updates centralized to ensure stable convergence.

Verifiability and Trust

In permissionless networks, “honesty” must be enforced. Zero-knowledge proofs and proof of learnability provide cryptographic guarantees: verifiers can randomly check whether reasoning processes were genuinely executed, reward signals are reproducible, and model weights are unaltered. This transforms the “trust problem” into a “mathematical problem.”

Token Incentive Feedback Mechanisms

Web3’s token economy turns traditional crowdsourcing into a self-regulating market:

• Participants earn rewards for contributing reasoning trajectories and high-quality feedback
• Staking mechanisms force participants to “put real money on the line” to guarantee work quality
• Slashing mechanisms immediately penalize cheating or misconduct
• The entire ecosystem naturally self-regulates under “profit-driven” incentives, without central authority

Multi-Agent Reinforcement Learning as an Ideal Experimental Field

Blockchain is inherently a transparent, continuously evolving multi-agent environment. Accounts, contracts, and agents continuously adapt strategies under incentives, providing an ideal sandbox for large-scale multi-agent reinforcement learning (MARL).

Frontiers of Decentralized Reinforcement Learning Practice

Prime Intellect: Engineering Breakthrough in Asynchronous RL

Prime Intellect has built a global open compute market and, through the prime-rl framework, achieved large-scale asynchronous distributed reinforcement learning.

Core innovation: complete decoupling—executors (rollout workers) and learners (trainers) no longer need to synchronize. Rollout workers continuously generate reasoning trajectories and upload them asynchronously; trainers pull data from shared buffers for gradient updates. Any GPU can join or leave at any time, without waiting.

Technical highlights:

• Integrates vLLM inference engine with PagedAttention and continuous batching for high throughput
• Uses FSDP2 parameter sharding and MoE sparse activation to efficiently run models with hundreds of billions of parameters
• GRPO+ reduces critic network overhead, naturally fitting asynchronous, high-latency environments
• OpenDiLoCo communication protocol reduces cross-region training communication by hundreds of times

Achievements: The INTELLECT series models achieve 98% utilization of heterogeneous cross-continental compute resources, with only 2% communication overhead. Despite using sparse activation (only 12B active parameters), INTELLECT-3 (106B MoE) approaches or surpasses larger closed-source models in inference performance.

Gensyn: From Swarm Collaboration to Verifiable Intelligence

Gensyn’s RL Swarm transforms decentralized RL into a “swarm” pattern: no central scheduler, nodes autonomously form a cycle of generation, evaluation, and update.

Three participant roles:

• Solvers: Local inference and rollout generation, supporting heterogeneous GPUs
• Proposers: Dynamically generate tasks (math problems, code challenges), supporting adaptive difficulty
• Evaluators: Use frozen “judge models” or rules to score rollouts, producing auditable rewards

Key algorithm: SAPO: “Shared rollout and filtering” rather than “shared gradients,” maintaining stable convergence in high-latency, heterogeneous environments. Compared to PPO’s critic-based or intra-group estimation methods, SAPO’s low bandwidth approach allows consumer-grade GPUs to participate effectively.

Verification system: Combining PoL and Verde mechanisms, ensuring each reasoning trajectory’s authenticity, providing a path for trillion-parameter models without reliance on tech giants.

Nous Research: From Models to Closed-Loop AI Ecosystems

Nous Research’s Hermes series and Atropos framework demonstrate a self-evolving AI system.

Model evolution path:

• Hermes 1-3: Use low-cost DPO for instruction alignment
• Hermes 4 / DeepHermes: Use chain-of-thought reasoning for System-2 style slow thinking, with rejection sampling and Atropos verification to build high-purity reasoning data
• Replacing PPO with GRPO enables reasoning RL to run on decentralized GPU networks like Psyche

Atropos’s role: Encapsulates prompts, tool calls, code execution, and multi-turn interactions into standardized RL environments, directly verifying output correctness and providing deterministic reward signals. In Psyche’s decentralized training network, Atropos acts as a “judge,” verifying whether nodes genuinely improved strategies, supporting verifiable proof of learnability.

DisTrO optimizer: Compresses RL training communication by several orders of magnitude, enabling household broadband to run large models with reinforcement learning—an “ultra-dimension reduction” against physical limits.

In Nous’s ecosystem, Atropos verifies reasoning chains, DisTrO compresses communication, Psyche runs RL cycles, and Hermes consolidates all learning into weights. Reinforcement learning becomes not just a training phase but a core protocol connecting data, environment, models, and infrastructure.

Gradient Network: Protocol Stack for Reinforcement Learning

Gradient defines the next-generation AI compute architecture via an “Open Intelligence Protocol Stack,” with the Echo framework as a dedicated RL optimizer.

Core design of Echo: decouples inference, training, and data paths, enabling independent scaling in heterogeneous environments. It adopts a “dual-group” architecture:

• Inference group: consumer-grade GPUs and edge devices, using Parallax pipeline for high-throughput sampling
• Training group: centralized or globally distributed GPU network, responsible for gradient updates and parameter synchronization

Synchronization protocols:

• Sequential pull: prioritize precision, training enforces model version updates on inference nodes
• Asynchronous push-pull: efficiency-oriented, inference generates trajectories with version tags, training consumes autonomously

This design maintains stable RL training over wide-area, high-latency networks, maximizing device utilization.

Grail in Bittensor Ecosystem: Cryptographic Verification of Reinforcement Learning

Bittensor’s unique Yuma consensus creates a large-scale, non-stationary reward function network. Covenant AI’s SN81 Grail subnet is the reinforcement learning engine within this ecosystem.

Grail’s core innovation: cryptographically prove each rollout’s authenticity and bind it to model identity. The three-layer mechanism:

1. Deterministic challenge generation: uses drand beacon and block hashes to produce unpredictable yet reproducible tasks (e.g., SAT solving, math reasoning), preventing precomputation cheating
2. Lightweight verification: via PRF sampling and sketch commitments, allows verifiers to efficiently check token-level log probabilities and reasoning chains, confirming they originate from claimed models
3. Model identity binding: links reasoning process with model fingerprint and token distribution signatures; any model substitution or replay is immediately detected

Results: Grail achieves a verifiable post-training process similar to GRPO, where miners generate multiple reasoning paths for the same problem, and verifiers score correctness, reasoning quality, and SAT satisfaction, then record normalized results on-chain as TAO weights. Experiments show that this framework boosts the math accuracy of Qwen2.5-1.5B from 12.7% to 47.6%, preventing cheating and significantly enhancing model capability.

Fraction AI: Competitive-Driven Reinforcement Learning

Fraction AI centers on competitive RL (RLFC) and gamified annotation, transforming static RLHF rewards into dynamic multi-agent adversarial interactions.

Core mechanisms:

• Agents: lightweight policies based on open-source LLMs, updated via QLoRA at low cost
• Spaces: isolated task domains, where agents pay to participate and earn rewards based on wins/losses
• AI Judges: real-time evaluation via RLAIF
• PoL: binds policy updates to competitive outcomes

Fundamentally: agents generate vast amounts of high-quality preference data through competition, guided by prompt engineering and hyperparameter tuning. This creates a “trustless fine-tuning” business loop, where data labeling becomes an automated, value-generating game.

General Paradigm and Differentiation Paths for Decentralized Reinforcement Learning

Convergent Architecture: Three-Layer Universal Design

Despite different entry points, the core logic of combining RL with Web3 exhibits a highly consistent “decouple–verify–incentivize” paradigm:

Layer 1: Physical Separation of Sampling and Training
Sparse, parallelizable rollout outsourcing to global consumer GPUs; high-bandwidth parameter updates centralized in a few training nodes. From Prime Intellect’s asynchronous actor-learner to Gradient’s dual-group architecture, this pattern is now standard.

Layer 2: Trust via Verification
In permissionless networks, computational authenticity must be cryptographically enforced. Examples include Gensyn’s PoL, Prime Intellect’s TopLoc, and Grail’s cryptographic proofs.

Layer 3: Tokenized Incentive Loop
Compute power, data generation, verification, and reward distribution form a self-regulating market. Rewards motivate participation; slashing deters cheating; the ecosystem maintains stability and continuous evolution through open incentives.

Differentiation and Moats

Projects choose different breakthroughs atop this shared architecture:

Algorithmic Innovation (Nous Research)
Aims to solve the fundamental bandwidth bottleneck in distributed training—compressing gradient communication by thousands of times with DisTrO, enabling household broadband to support large-scale RL. This is a “dimensionality reduction” attack on physical limits.

System Engineering (Prime Intellect, Gensyn, Gradient)
Focus on building the next-generation “AI runtime system.” Prime Intellect’s ShardCast, Gensyn’s RL Swarm, and Gradient’s Parallax are engineering efforts to maximize efficiency of heterogeneous clusters under current network conditions.

Market and Incentive Design (Bittensor, Fraction AI)
Focus on crafting incentive mechanisms that naturally lead nodes to discover optimal strategies, accelerating emergent intelligence. Grail’s cryptographic verification and Fraction AI’s competitive mechanisms exemplify this.

Opportunities and Challenges: The Future of Decentralized Reinforcement Learning

System-Level Advantages

Cost Structure Rewrites
RL’s infinite sampling demand allows Web3 to mobilize global long-tail GPU resources at minimal cost—estimated to reduce RL training costs by 50-80% compared to centralized clouds.

Sovereign Alignment
Breaking the monopoly of big tech on AI alignment. Communities can use token voting to define “what is a good answer,” democratizing AI governance. Reinforcement learning thus becomes a bridge between technology and community decision-making.

Structural Constraints

Bandwidth Wall
Despite innovations like DisTrO, physical latency still limits full-scale training of models with over 70B+ parameters. Currently, Web3 AI focuses more on fine-tuning and inference layers.

Reward Hacking Risks
In highly incentivized networks, nodes may overfit to reward signals rather than genuinely improving intelligence. Designing robust, cheat-resistant reward functions remains an ongoing game of mechanism design.

Byzantine Nodes
Nodes may manipulate training signals or poison the process. This requires continuous innovation in reward functions and adversarial training mechanisms.

Outlook: Rewriting the Production of Intelligence

The integration of reinforcement learning and Web3 fundamentally rewrites the mechanisms of “how intelligence is produced, aligned, and distributed.” Its evolutionary paths can be summarized into three complementary directions:

Decentralized Training Networks
From compute miners to policy networks, outsourcing parallel, verifiable rollouts to global long-tail GPU resources. Short-term focus on verifiable inference markets; mid-term evolution into task-clustered RL subnets.

Assetization of Preferences and Rewards
Transforming annotation labor into on-chain assets—preference feedback and reward models become governance and distribution assets, enabling high-quality feedback to be managed and allocated via tokens.

Vertical “Small and Beautiful” Specialization
In verifiable, quantifiable result niches—like DeFi strategies or code generation—small, specialized RL agents can directly optimize and capture value, potentially outperforming general-purpose closed-source models.

The real opportunity is not merely copying a decentralized version of OpenAI but rewriting the game rules: making training an open market, turning rewards and preferences into on-chain assets, and distributing the value of intelligent creation fairly among trainers, aligners, and users. This is the deepest significance of combining reinforcement learning with Web3.