The New Engine of Intelligent Awakening: How Reinforcement Learning Is Reshaping the AI Ecosystem of Web3

When DeepSeek-R1 was released, the industry truly realized an underestimated truth—reinforcement learning is not just a supporting role in model alignment, but the core driving force throughout the entire evolution of AI capabilities.

From pretraining’s “statistical pattern recognition” to post-training “structured reasoning,” and then to continuous alignment, reinforcement learning is becoming the key lever to unlock the next generation of intelligence. More interestingly, this mechanism naturally aligns with Web3’s decentralized incentive systems—this is no coincidence, but a resonance between two “incentive-driven systems” at their core.

This article will delve into how the technical architecture of reinforcement learning forms a closed loop with the distributed nature of blockchain, and by analyzing cutting-edge projects like Prime Intellect, Gensyn, Nous Research, Gradient, Grail, and Fraction AI, reveal the inevitability and imaginative space behind this wave.

The Three Tiers of Large Model Training: From Pretraining to Reasoning

The complete lifecycle of modern large models can be divided into three progressive stages, each redefining the boundaries of AI capabilities.

Pretraining is the forging of the foundation. Tens of thousands of H100 GPUs, synchronized globally, perform self-supervised learning on trillions of tokens, accounting for 80-95% of costs. This stage demands extreme network bandwidth, data consistency, and cluster homogeneity, all to be completed in highly centralized supercomputing centers—decentralization has no foothold here.

Supervised Fine-Tuning (SFT) is the targeted injection of capabilities. Fine-tuning the model on smaller instruction datasets costs only 5-15%. It can be full-parameter training or achieved via parameter-efficient methods like LoRA, Q-LoRA. While offering slightly higher decentralization potential, it still requires gradient synchronization, making it hard to break through network bottlenecks.

Post-Training Alignment is the main battlefield of reinforcement learning. This stage involves the lowest data volume and cost (only 5-10%), focusing on Rollout (inference trajectory sampling) and policy updates. Since Rollout naturally supports asynchronous distributed execution, nodes do not need to hold full weights. Combining verifiable computation and on-chain incentives, post-training becomes the most compatible with decentralization—this is precisely the starting point for Web3 + reinforcement learning.

Anatomy of Reinforcement Learning: The Power of the Triangle Loop

The core of reinforcement learning is a feedback loop: Policy generates actions → Environment returns rewards → Policy is iteratively optimized. This system typically comprises three key modules:

Policy Network acts as the decision center, generating actions based on states. During training, it requires centralized backpropagation to maintain numerical consistency, but during inference, it can be distributed to global nodes for parallel execution—this “separation of inference and training” is ideal for decentralized networks.

Experience Sampling (Rollout) is the data factory. Nodes locally execute the policy interacting with the environment, generating complete state-action-reward trajectories. Because sampling is highly parallel, with minimal communication and no hardware homogeneity requirements, consumer-grade GPUs, edge devices, and even smartphones can participate—this is the key to activating the vast global tail compute power.

Learner is the optimization engine, aggregating all Rollout data and performing gradient updates. This module demands the highest compute and bandwidth, usually running in centralized or lightly centralized clusters, but no longer requiring tens of thousands of GPUs like in pretraining.

The significance of this decoupled architecture is: it allows using cheap, globally distributed compute for Rollout, and a small amount of high-end compute for gradient updates. This is economically infeasible in traditional cloud models but becomes the optimal path in decentralized networks with on-chain incentives.

The Evolution of Reinforcement Learning Technologies: From RLHF to Verifiable Alignment

Reinforcement learning methodologies are evolving rapidly, and this process itself defines the feasible space for decentralization.

RLHF (Reinforcement Learning from Human Feedback) is the origin. It aligns models with human values through multiple candidate answers, human annotations, reward model training, and PPO policy optimization. Its fatal limitation is annotation cost: recruiting annotators, maintaining quality, handling disputes—these are bottlenecks in traditional setups.

RLAIF (AI Feedback Reinforcement Learning) breaks this bottleneck. Replacing human annotations with AI judges or rule-based systems makes preference signal generation automatable and scalable. Anthropic, OpenAI, and DeepSeek have adopted it as the mainstream paradigm. This shift is crucial for Web3—automation means it can be implemented via on-chain smart contracts.

GRPO (Group Relative Policy Optimization) is the core innovation of DeepSeek-R1. Unlike traditional PPO that requires an additional Critic network, GRPO models the advantage distribution within candidate answer groups, greatly reducing computational and memory costs. More importantly, it has stronger asynchronous fault tolerance, naturally adapting to multi-step network delays and node dropouts in distributed environments.

Verifiable Reinforcement Learning (RLVR) is the future direction. Introducing mathematical verification throughout the reward generation and utilization process ensures rewards come from reproducible rules and facts, rather than fuzzy human preferences. This is critical for permissionless networks—without verification, incentives can be “overfitted” by miners (score manipulation), risking system collapse.

The Technical Map of Six Cutting-Edge Projects

Prime Intellect: Engineering Limits of Asynchronous Reinforcement Learning

Prime Intellect aims to build a global open compute market, allowing any performance GPU to connect or disconnect at will, forming a self-healing compute network.

Its core is the prime-rl framework, a reinforcement learning engine tailored for distributed asynchronous environments. Traditional PPO requires all nodes to synchronize, causing global stalls if any node drops or delays; prime-rl abandons this synchronization paradigm, decoupling Rollout Workers from the Trainer.

The inference side (Rollout Worker) integrates the vLLM inference engine, leveraging its PagedAttention and batch processing for high throughput. The training side (Trainer) asynchronously pulls data from a shared experience replay buffer for gradient updates, without waiting for all workers.

INTELLECT family models demonstrate this framework’s capabilities:

• INTELLECT-1 (10B, October 2024): first to prove cross-continental heterogeneous network training feasible, with less than 2% communication overhead and 98% hardware utilization
• INTELLECT-2 (32B, April 2025): the first “permissionless RL” model, validating stable convergence under multi-step delays and asynchronous environments
• INTELLECT-3 (106B MoE, November 2025): uses only 12B active parameters in a sparse architecture, trained on 512×H200 hardware, approaching or surpassing larger closed-source models (AIME 90.8%, GPQA 74.4%, MMLU-Pro 81.9%)

Supporting these models are OpenDiLoCo communication protocol (reducing cross-region training communication by hundreds of times) and TopLoc verification mechanism (using activation fingerprints and sandbox verification to ensure inference authenticity). These components collectively prove a key proposition: decentralized reinforcement learning training is not only feasible but can produce world-class intelligent models.

Gensyn: Swarm Intelligence of “Generate-Evaluate-Update”

Gensyn’s philosophy is closer to “sociology”—it’s not just task distribution and result aggregation, but simulating human social collaborative learning.

RL Swarm decomposes the core RL loop into a P2P organization of three roles:

• Solvers perform local inference and Rollout generation; hardware differences among nodes are irrelevant
• Proposers dynamically generate tasks (math problems, coding challenges, etc.), supporting curriculum learning-style difficulty adaptation
• Evaluators use frozen “judge models” or rules to evaluate local Rollouts, generating local rewards

These form a closed loop without central coordination. Even better, this structure naturally maps onto blockchain networks—miners are Solvers, stakers are Evaluators, DAOs are Proposers.

SAPO (Swarm Sampling Policy Optimization) is an optimization algorithm designed for this system. Its core idea is “sharing Rollouts, not sharing gradients”—each node samples from a global Rollout pool, treating it as local data, maintaining stable convergence in environments with no central coordination and high latency. Compared to Critic-based PPO or group advantage-based GRPO, SAPO enables effective large-scale reinforcement learning with minimal bandwidth, allowing consumer-grade GPUs to participate.

Nous Research: Closed-Loop Ecosystem for Verifiable Inference Environments

Nous Research is not just building an RL system but constructing a continuously self-evolving cognitive infrastructure.

Its core components resemble gears in a precise machine: Hermes (model interface) → Atropos (verification environment) → DisTrO (communication compression) → Psyche (decentralized network) → World Sim (complex simulation) → Forge (data collection).

Atropos is the key—encapsulating prompts, tool calls, code execution, multi-turn interactions into standardized RL environments that can directly verify output correctness, providing deterministic reward signals. This eliminates reliance on costly, non-scalable human annotations.

More importantly, in the decentralized network Psyche, Atropos acts as a “trusted arbiter.” Through verifiable computation and on-chain incentives, it can prove whether each node truly improved the policy, supporting a Proof-of-Learning mechanism. This fundamentally addresses the most challenging issue in distributed RL—the trustworthiness of reward signals.

DisTrO optimizer aims to solve the core bottleneck of distributed training: bandwidth. Through gradient compression and momentum decoupling, it reduces communication costs by several orders of magnitude, enabling household broadband to run large models. Coupled with Psyche’s on-chain scheduling, this combination makes distributed RL a “reality” rather than an “ideal.”

Gradient Network: Open Intelligence Protocol Stack

Gradient’s perspective is more macro—building a complete “Open Intelligence Protocol Stack,” covering modules from low-level communication to top-level applications.

Echo is its reinforcement learning training framework, designed to decouple training, inference, and data paths in RL, enabling independent scaling in heterogeneous environments.

Echo adopts a “dual swarm architecture” for inference and training:

• Inference Swarm: consumer-grade GPUs and edge devices, utilizing Parallax distributed inference engine for high-throughput sampling
• Training Swarm: GPUs distributed globally, responsible for gradient updates and parameter synchronization

These two operate independently. To maintain policy and data consistency, Echo provides two synchronization protocols:

• Sequential Pull Mode (accuracy-first): training nodes force inference nodes to refresh models before pulling new trajectories, ensuring freshness
• Asynchronous Push-Pull Mode (efficiency-first): inference nodes continuously generate versioned trajectories, training nodes consume at their own pace, maximizing device utilization

This mechanism makes global, heterogeneous RL training feasible while maintaining convergence stability.

Grail and Bittensor: Cryptography-Driven Trust Layer

Bittensor, via its Yuma consensus mechanism, constructs a vast, sparse, non-stationary reward function network. SN81 Grail builds upon this to create a verifiable execution layer for reinforcement learning.

Grail aims to cryptographically prove the authenticity of each RL rollout and bind it to the model identity. Its mechanism has three layers:

1. Deterministic Challenge Generation: using drand randomness beacon and block hashes to generate unpredictable yet reproducible challenges (e.g., SAT, GSM8K), preventing precomputation cheating
2. Low-Cost Sampling Verification: via PRF-based sampling and sketch commitments, verifiers can verify token-level log probabilities and inference chains at minimal cost, confirming rollouts are generated by claimed models
3. Model Identity Binding: linking inference process with model weight fingerprints, ensuring model replacement or replay detection

With this system, Grail enables verifiable post-training like GRPO: miners generate multiple reasoning paths for the same prompt, and verifiers score correctness and reasoning quality, writing normalized results on-chain. Public experiments show this framework has increased Qwen2.5-1.5B’s MATH accuracy from 12.7% to 47.6%, effectively preventing cheating and significantly enhancing model capability.

Fraction AI: Emergence of Intelligence in Competition

Fraction AI’s innovation rewrites the RLHF paradigm—replacing static rewards and manual annotations with open, dynamic competitive environments.

Agents compete within different Spaces (isolated task domains), with relative rankings and AI judge scores forming real-time rewards. This transforms alignment into a continuous multi-agent game, where rewards come from evolving opponents and evaluators, inherently preventing reward model exploitation.

Four key system components:

• Agents: lightweight policy units based on open-source LLMs, updated via QLoRA at low cost
• Spaces: isolated task environments, where agents pay to participate and earn rewards based on wins/losses
• AI Judges: RLAIF-based real-time reward layer, providing decentralized evaluation
• Proof-of-Learning: binds policy updates to specific competition outcomes, ensuring verifiability

Essentially, Fraction AI creates a “human-machine co-evolution engine.” Users guide exploration via prompt engineering, agents autonomously generate vast high-quality preference data through microscopic competition, ultimately forming a “trustless fine-tuning” business loop.

Convergent Architectural Logic: Why Reinforcement Learning and Web3 Are Inevitable to Meet

Despite different entry points, the underlying architectural logic of these projects is astonishingly consistent, converging into: Decouple - Verify - Incentivize.

Decoupling is the default topology. Sparse communication Rollouts are outsourced to global consumer-grade GPUs, while high-bandwidth parameter updates are centralized in a few nodes. This physical separation naturally matches the heterogeneity of decentralized networks.

Verification is the infrastructure. The authenticity of computation must be guaranteed through mathematics and mechanism design—via verifiable inference, Proof-of-Learning, cryptographic proofs. These not only solve trust issues but also become core competitive advantages in decentralized networks.

Incentives are the self-evolving engine. Compute supply, data generation, and reward distribution form a closed loop—participants are rewarded with tokens, and cheating is suppressed via slashing—keeping the network stable and continuously evolving in an open environment.

The Endgame Imagination: Three Parallel Evolution Paths

The integration of reinforcement learning and Web3 offers a real opportunity not just to replicate a decentralized OpenAI, but to fundamentally rewrite the “production relations” of intelligence.

Path One: Decentralized Training Networks will outsource parallel, verifiable Rollouts to long-tail GPUs worldwide, initially focusing on verifiable inference markets, then evolving into task-clustered reinforcement learning sub-networks.

Path Two: Assetization of Preferences and Rewards will encode and govern preferences and rewards on-chain, transforming high-quality feedback and reward models into tradable data assets, elevating participants from “annotation labor” to “data equity holders.”

Path Three: Niche, Small-and-Beautiful Evolution will cultivate small, powerful RL agents in verifiable, quantifiable-result vertical scenarios—DeFi strategy executors, code generators, mathematical solvers—where policy improvements and value capture are directly linked.

All three paths point toward the same ultimate: training no longer remains the exclusive domain of big corporations; reward and value distribution become transparent and democratic. Every participant contributing compute, data, or verification can earn corresponding returns. The convergence of reinforcement learning and Web3 is fundamentally about redefining “who owns AI” through code and incentives.