IOSG | From Computing Power to Intelligence: Reinforcement Learning-Driven Decentralized AI Investment Map
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IOSG | From Computing Power to Intelligence: Reinforcement Learning-Driven Decentralized AI Investment Map
Systematically deconstructs AI training paradigms and the technical principles of reinforcement learning, demonstrating the structural advantages of reinforcement learning × Web3.
Navigating Web3 tides with focused insightsAuthor: Jacob Zhao @IOSG
Artificial intelligence is evolving from statistical learning centered on "pattern fitting" to a capability system built around "structured reasoning," with post-training gaining rapidly in importance. The emergence of DeepSeek-R1 marks a paradigm-level breakthrough for reinforcement learning in the era of large models. Industry consensus has formed: pre-training establishes the foundational general capabilities of models, while reinforcement learning is no longer merely a tool for value alignment—it has been proven capable of systematically enhancing reasoning chain quality and complex decision-making abilities, gradually evolving into a technical pathway for continuously elevating intelligence levels.
Meanwhile, Web3 is reshaping AI's production relationships through decentralized compute networks and cryptographic incentive systems. Reinforcement learning’s structural demands—rollout sampling, reward signals, and verifiable training—naturally align with blockchain’s strengths in compute collaboration, incentive distribution, and verifiable execution. This report systematically dissects AI training paradigms and reinforcement learning principles, argues for the structural advantages of reinforcement learning × Web3, and analyzes projects including Prime Intellect, Gensyn, Nous Research, Gradient, Grail, and Fraction AI.
The Three Stages of AI Training: Pre-training, Instruction Fine-tuning, and Post-training Alignment
The full lifecycle of modern large language model (LLM) training is typically divided into three core stages: pre-training, supervised fine-tuning (SFT), and post-training (post-training/RL). These stages respectively fulfill the functions of “building a world model—injecting task-specific capabilities—shaping reasoning and values.” Their computational structures, data requirements, and verification difficulties determine their suitability for decentralization.
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Pre-training constructs the model’s linguistic statistical structure and cross-modal world model via large-scale self-supervised learning, forming the foundation of LLM capabilities. This stage requires global synchronous training on trillions of tokens using homogeneous clusters of thousands to tens of thousands of H100 GPUs, accounting for 80–95% of total costs. It is highly sensitive to bandwidth and data copyright, necessitating completion in a highly centralized environment.
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Fine-tuning (Supervised Fine-tuning) injects task-specific abilities and instruction formats. With small data volumes and cost占比 around 5–15%, it can be performed as full-parameter training or via parameter-efficient fine-tuning (PEFT) methods such as LoRA, Q-LoRA, and Adapter—the mainstream approaches in industry. However, gradient synchronization remains required, limiting its potential for decentralization.
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Post-training consists of multiple iterative sub-stages that shape the model’s reasoning ability, values, and safety boundaries. Methods include reinforcement learning frameworks (RLHF, RLAIF, GRPO), preference optimization without RL (DPO), and process reward models (PRM). This stage has lower data volume and cost占比 (5–10%), primarily focused on rollout and policy updates. It naturally supports asynchronous and distributed execution, does not require nodes to hold full weights, and combined with verifiable computing and on-chain incentives, can form an open decentralized training network—the phase most suited to Web3 integration.
Reinforcement Learning Technology Landscape: Architecture, Frameworks, and Applications
System Architecture and Core Components of Reinforcement Learning
Reinforcement Learning (RL) drives autonomous improvement of decision-making through “environment interaction—reward feedback—policy update.” Its core structure forms a feedback loop composed of state, action, reward, and policy. A complete RL system typically includes three components: Policy (policy network), Rollout (experience sampling), and Learner (policy updater). The policy interacts with the environment to generate trajectories, and the Learner updates the policy based on reward signals, creating a continuous, iterative optimization process:
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Policy Network: Generates actions from environmental states and serves as the decision-making core. During training, centralized backpropagation is required to maintain consistency; during inference, it can be distributed across nodes for parallel execution.
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Experience Sampling (Rollout): Nodes execute environment interactions based on the policy, generating trajectories of state-action-reward sequences. This process is highly parallelizable, requires minimal communication, and is insensitive to hardware differences—making it the most suitable component for decentralized scaling.
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Learner: Aggregates all rollout trajectories and performs policy gradient updates. As the module with the highest demands on compute power and bandwidth, it is typically deployed centrally or lightly centralized to ensure convergence stability.
Evolution of Reinforcement Learning Frameworks (RLHF → RLAIF → PRM → GRPO)
Reinforcement learning can generally be divided into five stages, with the overall workflow described below:
#Data Generation Phase (Policy Exploration)
Given input prompts, the policy model πθ generates multiple candidate reasoning chains or complete trajectories, providing the sample basis for subsequent preference evaluation and reward modeling, determining the breadth of policy exploration.
#Preference Feedback Phase (RLHF / RLAIF)
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RLHF (Reinforcement Learning from Human Feedback) uses multiple candidate responses, human preference labeling, trains a reward model (RM), and optimizes the policy via PPO to make model outputs better align with human values. It was a key step in the transition from GPT-3.5 to GPT-4.
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RLAIF (Reinforcement Learning from AI Feedback) replaces human annotation with AI Judges or constitutional rules, automating preference acquisition, significantly reducing costs and enabling scalability. It has become the mainstream alignment paradigm at Anthropic, OpenAI, and DeepSeek.
#Reward Modeling Phase (Reward Modeling)
Preference pairs are fed into a reward model to learn mappings from outputs to rewards. RM teaches the model “what a correct answer is,” while PRM teaches it “how to reason correctly.”
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RM (Reward Model): Evaluates the quality of final answers by scoring outputs only.
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Process Reward Model (PRM): Instead of evaluating only final answers, PRM scores each reasoning step, token, and logical segment. It is a key technology behind OpenAI o1 and DeepSeek-R1, essentially “teaching the model how to think.”
#Reward Verification Phase (RLVR / Reward Verifiability)
Introduces “verifiable constraints” into the generation and use of reward signals, ensuring rewards derive as much as possible from reproducible rules, facts, or consensus. This reduces risks of reward hacking and bias, enhancing auditability and scalability in open environments.
#Policy Optimization Phase (Policy Optimization)
Updates policy parameters θ under guidance from reward model signals to obtain a new policy πθ′ with stronger reasoning, higher safety, and more stable behavior. Mainstream optimization methods include:
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PPO (Proximal Policy Optimization): The traditional optimizer for RLHF, known for stability but often facing slow convergence and insufficient stability in complex reasoning tasks.
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GRPO (Group Relative Policy Optimization): A core innovation of DeepSeek-R1, modeling the advantage distribution within candidate answer groups to estimate expected value rather than relying on simple ranking. This method preserves reward magnitude information, is better suited for reasoning chain optimization, leads to more stable training, and is regarded as an important RL optimization framework beyond PPO for deep reasoning scenarios.
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DPO (Direct Preference Optimization): A non-RL post-training method that skips trajectory generation and reward modeling, directly optimizing over preference pairs. Low-cost and stable, it is widely used for aligning open-source models like Llama and Gemma, though it does not enhance reasoning ability.
#New Policy Deployment Phase (New Policy Deployment)
The optimized model exhibits stronger reasoning chain generation (System-2 Reasoning), behaviors better aligned with human or AI preferences, lower hallucination rates, and higher safety. Through continuous iteration, the model learns preferences, refines processes, and improves decision quality in a closed loop.
Five Major Industrial Applications of Reinforcement Learning
Reinforcement Learning (RL) has evolved from early game-playing intelligence into a core framework for autonomous decision-making across industries. Based on technical maturity and industrial adoption, its applications can be categorized into five major types, each driving key breakthroughs in their respective domains.
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Game & Strategy Systems: The earliest validated domain for RL, where RL demonstrated human-expert-level or superior decision intelligence in “perfect information + clear rewards” environments such as AlphaGo, AlphaZero, AlphaStar, and OpenAI Five, laying the foundation for modern RL algorithms.
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Robotics & Embodied AI: RL enables robots to learn manipulation, motion control, and cross-modal tasks (e.g., RT-2, RT-X) through continuous control, dynamics modeling, and environmental interaction. It is rapidly advancing toward industrialization and is a key technical route for real-world robot deployment.
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Digital Reasoning / LLM System-2: RL + PRM drives large models from “language imitation” to “structured reasoning,” exemplified by DeepSeek-R1, OpenAI o1/o3, Anthropic Claude, and AlphaGeometry. The essence lies in reward optimization at the reasoning chain level, not just final answer evaluation.
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Automated Scientific Discovery & Mathematical Optimization: RL discovers optimal structures or strategies in unlabelled, complex-reward, and vast search spaces, achieving foundational breakthroughs like AlphaTensor, AlphaDev, and Fusion RL, demonstrating exploration capabilities surpassing human intuition.
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Economic Decision-making & Trading Systems: RL is used for strategy optimization, high-dimensional risk control, and adaptive trading system generation. Compared to traditional quantitative models, it excels at continuous learning in uncertain environments and is a crucial component of intelligent finance.
The Natural Fit Between Reinforcement Learning and Web3
The strong alignment between Reinforcement Learning (RL) and Web3 stems from both being fundamentally “incentive-driven systems.” RL relies on reward signals to optimize policies, while blockchains use economic incentives to coordinate participant behavior—resulting in natural consistency at the mechanism level. RL’s core needs—large-scale heterogeneous rollouts, reward distribution, and authenticity verification—are precisely where Web3 holds structural advantages.
#Decoupling of Inference and Training
RL training can be clearly split into two phases:
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Rollout (Exploration Sampling): The model generates large amounts of data based on current policy—a compute-intensive but communication-sparse task. It does not require frequent inter-node communication, making it ideal for parallel generation across globally distributed consumer-grade GPUs.
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Update (Parameter Update): Updating model weights based on collected data requires high-bandwidth centralized nodes.
This “inference–training decoupling” naturally fits decentralized heterogeneous compute architectures: Rollouts can be outsourced to open networks and settled via token mechanisms based on contribution, while model updates remain centralized to ensure stability.
#Verifiability
ZK and Proof-of-Learning provide means to verify whether nodes have genuinely executed inference, solving honesty issues in open networks. For deterministic tasks like code or math reasoning, verifiers can confirm work simply by checking answers, greatly enhancing the credibility of decentralized RL systems.
#Incentive Layer: Token-Based Feedback Production Mechanism
Web3’s token mechanisms can directly reward contributors to RLHF/RLAIF preference feedback, creating a transparent, settleable, permissionless incentive structure for preference data generation. Staking and slashing further constrain feedback quality, forming a more efficient and aligned feedback market than traditional crowdsourcing.
#Potential for Multi-Agent Reinforcement Learning (MARL)
Blockchains are inherently public, transparent, and continuously evolving multi-agent environments, where accounts, contracts, and agents constantly adjust strategies under incentive pressure—making them naturally suitable as large-scale MARL testbeds. Though still early, their publicly accessible state, verifiable execution, and programmable incentives offer fundamental advantages for future MARL development.
Analysis of Classic Web3 + Reinforcement Learning Projects
Based on the above theoretical framework, we provide brief analyses of the most representative projects in the current ecosystem:
Prime Intellect: Asynchronous Reinforcement Learning Paradigm prime-rl
Prime Intellect aims to build a globally open compute market, lower training barriers, promote collaborative decentralized training, and develop a full-stack open superintelligence technology stack. Its system includes: Prime Compute (unified cloud/distributed compute environment), the INTELLECT model family (10B–100B+), an open reinforcement learning environment hub (Environments Hub), and a large-scale synthetic data engine (SYNTHETIC-1/2).
Prime Intellect’s core infrastructure component, the prime-rl framework, is designed specifically for asynchronous distributed environments and closely related to reinforcement learning, along with other innovations such as the OpenDiLoCo communication protocol (breaking bandwidth bottlenecks) and the TopLoc verification mechanism (ensuring computational integrity).
#Overview of Prime Intellect’s Core Infrastructure Components
#Technical Foundation: prime-rl Asynchronous Reinforcement Learning Framework
prime-rl is Prime Intellect’s core training engine, designed for large-scale asynchronous decentralized environments. It achieves high-throughput inference and stable updates by fully decoupling Actor–Learner roles. Rollout Workers and Trainers no longer block synchronously—nodes can join or leave anytime, continuously pulling the latest policy and uploading generated data:
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Actors (Rollout Workers): Responsible for model inference and data generation. Prime Intellect innovatively integrates the vLLM inference engine into Actors. vLLM’s PagedAttention and continuous batching enable extremely high throughput in generating reasoning trajectories.
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Learners (Trainers): Handle policy optimization. Learners asynchronously pull data from a shared experience replay buffer for gradient updates, without waiting for all Actors to finish the current batch.
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Orchestrator: Manages scheduling of model weights and data flow.
#Key Innovations of prime-rl
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True Asynchrony: prime-rl abandons the synchronous paradigm of traditional PPO—no waiting for slow nodes, no batch alignment—enabling any number and performance level of GPUs to connect anytime, establishing feasibility for decentralized RL.
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Deep Integration of FSDP2 and MoE: Through FSDP2 parameter sharding and MoE sparse activation, prime-rl enables efficient training of hundred-billion-parameter models in distributed settings. Actors run only active experts, significantly reducing VRAM and inference costs.
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GRPO+: GRPO eliminates the Critic network, greatly reducing computation and VRAM overhead, and naturally suits asynchronous environments. prime-rl’s GRPO+ adds stabilization mechanisms to ensure reliable convergence even under high latency.
#INTELLECT Model Family: A Milestone in Decentralized RL Maturity
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INTELLECT-1 (10B, Oct 2024): First proved OpenDiLoCo could efficiently train across heterogeneous networks spanning three continents (communication占比 <2%, compute utilization 98%), redefining physical limits of cross-regional training;
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INTELLECT-2 (32B, Apr 2025): As the first permissionless RL model, it verified prime-rl and GRPO+’s stable convergence under multi-step delays and asynchronous conditions, enabling global open compute participation in decentralized RL;
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INTELLECT-3 (106B MoE, Nov 2025): Uses a sparse architecture activating only 12B parameters, trained on 512×H200 with flagship-level reasoning performance (AIME 90.8%, GPQA 74.4%, MMLU-Pro 81.9%), performing on par with or exceeding much larger centralized closed models.
Beyond these, Prime Intellect has built several supporting infrastructures: OpenDiLoCo reduces intercontinental training communication by hundreds of times via time-sparse communication and quantized weight deltas, allowing INTELLECT-1 to maintain 98% utilization across three continents; TopLoc + Verifiers create a decentralized trusted execution layer, using activation fingerprints and sandbox verification to ensure authenticity of inference and reward data; the SYNTHETIC data engine produces large-scale high-quality reasoning chains, enabling a 671B model to run efficiently on consumer GPU clusters via pipeline parallelism. These components provide critical engineering foundations for data generation, verification, and inference throughput in decentralized RL. The INTELLECT series proves this tech stack can produce mature world-class models, marking decentralized training systems' transition from concept to practicality.
Gensyn: Core RL Stack – RL Swarm and SAPO
Gensyn aims to aggregate global idle compute into an open, trustless, infinitely scalable AI training infrastructure. Its core includes a cross-device standardized execution layer, peer-to-peer coordination network, and trustless task verification system, with smart contracts automatically assigning tasks and rewards. Leveraging RL characteristics, Gensyn introduces core mechanisms like RL Swarm, SAPO, and SkipPipe, decoupling generation, evaluation, and update phases, using a global swarm of heterogeneous GPUs to achieve collective evolution. What it delivers is not just raw compute, but verifiable intelligence.
#Gensyn Stack Applied to Reinforcement Learning
#RL Swarm: Decentralized Collaborative Reinforcement Learning Engine
RL Swarm introduces a novel collaborative mode—not mere task distribution, but a decentralized “generate–evaluate–update” cycle mimicking human social learning, running in infinite loops:
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Solvers (Executors): Handle local model inference and Rollout generation—node heterogeneity poses no issue. Gensyn integrates high-throughput inference engines (e.g., CodeZero) locally, outputting full trajectories, not just answers.
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Proposers (Task Creators): Dynamically generate tasks (math problems, coding challenges, etc.), supporting task diversity and difficulty adaptation akin to curriculum learning.
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Evaluators (Assessors): Use frozen “judge models” or rules to evaluate local rollouts, generating local reward signals. Evaluation is auditable, reducing room for malicious behavior.
Together, they form a P2P RL organizational structure capable of large-scale collaborative learning without centralized coordination.
#SAPO: Policy Optimization Re-engineered for Decentralization
SAPO (Swarm Sampling Policy Optimization) centers on “sharing rollouts and filtering out gradient-free samples, rather than sharing gradients.” By leveraging massive decentralized rollout sampling and treating received rollouts as locally generated, SAPO maintains stable convergence in environments without central coordination and with significant node latency differences. Compared to PPO (which relies on Critic networks and high compute cost) or GRPO (based on group advantage estimation), SAPO enables consumer-grade GPUs to effectively participate in large-scale RL optimization with minimal bandwidth.
Through RL Swarm and SAPO, Gensyn demonstrates that reinforcement learning (especially RLVR in post-training) is naturally suited to decentralized architectures—because it relies more on massive, diverse exploration (rollout) than high-frequency parameter synchronization. Combined with PoL and Verde’s verification systems, Gensyn offers an alternative path for training trillion-parameter models—one no longer dependent on a single tech giant, but a self-evolving superintelligent network composed of millions of heterogeneous GPUs worldwide.
Nous Research: Verifiable Reinforcement Learning Environment Atropos
Nous Research is building a decentralized, self-evolving cognitive infrastructure. Its core components—Hermes, Atropos, DisTrO, Psyche, and World Sim—are organized into a continuous, closed-loop intelligent evolution system. Unlike the traditional linear flow of “pre-training → post-training → inference,” Nous employs DPO, GRPO, rejection sampling, and other RL techniques to unify data generation, validation, learning, and inference into a continuous feedback loop, creating a self-improving AI ecosystem.
#Overview of Nous Research Components
#Model Layer: Hermes and the Evolution of Reasoning Capabilities
Hermes series serve as Nous Research’s primary user-facing model interface, clearly illustrating the industry’s shift from traditional SFT/DPO alignment to reasoning-focused reinforcement learning (Reasoning RL):
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Hermes 1–3: Instruction alignment and early agent capabilities. Hermes 1–3 achieved robust instruction alignment via low-cost DPO, with Hermes 3 incorporating synthetic data and the first implementation of Atropos verification.
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Hermes 4 / DeepHermes: Embeds System-2-style slow thinking into weights via chain-of-thought, enhances math and code performance with Test-Time Scaling, and relies on “rejection sampling + Atropos verification” to build high-purity reasoning data.
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DeepHermes further adopts GRPO instead of PPO—which is hard to deploy distributively—enabling reasoning RL to run on Psyche’s decentralized GPU network, laying the engineering foundation for scalable open-source reasoning RL.
#Atropos: Verifiable Reward-Driven Reinforcement Learning Environment
Atropos is the true hub of Nous’s RL system. It encapsulates prompts, tool calls, code execution, and multi-turn interactions into standardized RL environments, directly verifying output correctness to provide deterministic reward signals—replacing costly and non-scalable human annotations. More importantly, within the decentralized training network Psyche, Atropos acts as the “referee,” verifying whether nodes genuinely improve strategies and supporting auditable Proof-of-Learning, fundamentally solving the reward credibility problem in distributed RL.
#DisTrO and Psyche: Optimizer Layer for Decentralized Reinforcement Learning
Traditional RLF (RLHF/RLAIF) training relies on centralized high-bandwidth clusters—a core barrier open-source efforts cannot replicate. DisTrO reduces RL’s communication cost by orders of magnitude via momentum decoupling and gradient compression, enabling training over internet bandwidth. Psyche deploys this training mechanism on a blockchain network, allowing nodes to perform inference, verification, reward assessment, and weight updates locally, forming a complete RL loop.
In Nous’s system, Atropos verifies reasoning chains; DisTrO compresses training communication; Psyche runs the RL loop; World Sim provides complex environments; Forge collects real reasoning; Hermes writes all learning into weights. Reinforcement learning is not just a training phase—it is the core protocol in Nous’s architecture connecting data, environment, model, and infrastructure, making Hermes a living system capable of continuous self-improvement on open compute networks.
Gradient Network: Reinforcement Learning Architecture Echo
Gradient Network’s core vision is to reshape AI’s computing paradigm through an “Open Intelligence Stack.” Gradient’s tech stack comprises a set of independently evolvable yet heterogeneously cooperative core protocols. From bottom to top, the stack includes: Parallax (distributed inference), Echo (decentralized RL training), Lattica (P2P network), SEDM / Massgen / Symphony / CUAHarm (memory, collaboration, security), VeriLLM (trusted verification), Mirage (high-fidelity simulation)—together forming a continuously evolving decentralized intelligent infrastructure.
Echo – Reinforcement Learning Training Architecture
Echo is Gradient’s reinforcement learning framework, whose core design principle is decoupling training, inference, and data (reward) paths in RL, enabling independent scaling and scheduling of rollout generation, policy optimization, and reward evaluation in heterogeneous environments. It operates collaboratively in a heterogeneous network of inference-side and training-side nodes, using lightweight synchronization to maintain training stability in wide-area heterogeneous settings, effectively mitigating SPMD failure and GPU utilization bottlenecks caused by mixing inference and training in traditional DeepSpeed RLHF / VERL.
Echo uses a “dual-swarm inference–training architecture” to maximize compute utilization, with both swarms operating independently and non-blocking:
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Maximize sampling throughput: The Inference Swarm, composed of consumer-grade GPUs and edge devices, uses Parallax with pipeline-parallelism to build high-throughput samplers focused on trajectory generation;
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Maximize gradient compute: The Training Swarm, which can run on centralized clusters or global consumer GPU networks, handles gradient updates, parameter synchronization, and LoRA fine-tuning, focusing on the learning process.
To maintain consistency between policy and data, Echo provides two lightweight synchronization protocols—Sequential and Asynchronous—for bidirectional consistency management of policy weights and trajectories:
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Sequential Pull Mode | Accuracy-first: Before pulling new trajectories, the training side forces inference nodes to refresh model versions, ensuring trajectory freshness—ideal for tasks highly sensitive to stale policies;
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Asynchronous Push–Pull Mode | Efficiency-first: Inference nodes continuously generate version-tagged trajectories, and the training side consumes at its own pace. The orchestrator monitors version drift and triggers weight refreshes, maximizing device utilization.
Underlying this, Echo builds upon Parallax (heterogeneous inference under low bandwidth) and lightweight distributed training components (e.g., VERL), relying on LoRA to reduce cross-node sync costs, enabling stable RL operation over global heterogeneous networks.
Grail: Reinforcement Learning in the Bittensor Ecosystem
Bittensor, through its unique Yuma consensus mechanism, constructs a vast, sparse, non-stationary reward function network.
Within the Bittensor ecosystem, Covenant AI builds an integrated vertical pipeline from pre-training to RL post-training via SN3 Templar, SN39 Basilica, and SN81 Grail. SN3 Templar handles base model pre-training, SN39 Basilica provides a distributed compute marketplace, and SN81 Grail serves as the “verifiable reasoning layer” for RL post-training, carrying out core RLHF/RLAIF processes and completing the closed-loop optimization from base model to aligned policy.
GRAIL aims to cryptographically prove the authenticity of every reinforcement learning rollout and bind it to model identity, ensuring RLHF can be securely executed in a trustless environment. The protocol establishes a trust chain through three mechanisms:
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Deterministic Challenge Generation: Uses drand random beacons and block hashes to generate unpredictable yet reproducible challenge tasks (e.g., SAT, GSM8K), preventing pre-computation cheating;
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PRF-indexed sampling and sketch commitments allow verifiers to spot-check token-level logprob and reasoning chains at very low cost, confirming rollouts were genuinely generated by the claimed model;
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Model Identity Binding: Links inference processes to model weight fingerprints and structural signatures of token distributions, ensuring model swaps or result replays are immediately detected. This provides a foundation of authenticity for RL rollouts.
On this foundation, the Grail subnet implements a GRPO-style verifiable post-training process: miners generate multiple reasoning paths for the same problem, validators score based on correctness, reasoning quality, and SAT satisfaction, and normalized results are written on-chain as TAO weights. Public experiments show this framework increased Qwen2.5-1.5B’s MATH accuracy from 12.7% to 47.6%, proving it can prevent cheating while significantly boosting model capability. Within Covenant AI’s training stack, Grail is the trust and execution cornerstone for decentralized RLVR/RLAIF, though it has not yet launched on mainnet.
Fraction AI: Competition-Based Reinforcement Learning RLFC
Fraction AI’s architecture is explicitly built around Reinforcement Learning from Competition (RLFC) and gamified data labeling, replacing the static rewards and manual annotations of traditional RLHF with an open, dynamic competitive environment. Agents compete in different Spaces, with relative rankings and AI judge scores jointly forming real-time rewards, turning the alignment process into a continuous online multi-agent game system.
Core differences between traditional RLHF and Fraction AI’s RLFC:
The core value of RLFC lies in rewards no longer coming from a single model, but from evolving opponents and evaluators, avoiding reward exploitation and preventing the ecosystem from falling into local optima through strategic diversity. The structure of Spaces determines the nature of the game (zero-sum or positive-sum), driving emergent complex behaviors through competition and cooperation.
In system architecture, Fraction AI decomposes the training process into four key components:
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Agents: Lightweight policy units based on open-source LLMs, extended via QLoRA with differential weights for low-cost updates;
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Spaces: Isolated task-domain environments where agents pay to enter and earn rewards based on wins/losses;
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AI Judges: An instant reward layer built on RLAIF, providing scalable, decentralized evaluation;
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Proof-of-Learning: Binds strategy updates to specific competition outcomes, ensuring training is verifiable and cheat-proof.
Fraction AI’s essence is building a “human-machine collaborative evolutionary engine.” Users act as “meta-optimizers” at the strategy layer, guiding exploration via prompt engineering and hyperparameter configuration; meanwhile, agents autonomously generate massive volumes of high-quality preference pairs through micro-competition. This model achieves a commercial loop through “trustless fine-tuning” in data annotation.
Comparison of Reinforcement Learning Web3 Project Architectures
Conclusion and Outlook: Paths and Opportunities for Reinforcement Learning × Web3
Based on deconstructive analysis of the above frontier projects, we observe: despite differing entry points (algorithm, engineering, or market), when reinforcement learning (RL) combines with Web3, their underlying architectural logic converges on a highly consistent “decouple–verify–incentivize” paradigm. This is not a technical coincidence, but an inevitable outcome of decentralized networks adapting to RL’s unique properties.
Common Architectural Features of Reinforcement Learning: Addressing core physical and trust constraints
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Physical Separation of Inference and Training (Decoupling of Rollouts & Learning) — Default Computational Topology
Communication-sparse, parallelizable rollouts are outsourced to global consumer-grade GPUs, while high-bandwidth parameter updates are centralized on a few training nodes—true for both Prime Intellect’s asynchronous Actor–Learner and Gradient Echo’s dual-swarm architecture.
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Verification-Driven Trust Layer — Infrastructure-Level Solution
In permissionless networks, computational authenticity must be enforced through mathematical and mechanistic design. Examples include Gensyn’s PoL, Prime Intellect’s TOPLOC, and Grail’s cryptographic verification.
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Tokenized Incentive Loop — Market Self-Regulation
Compute supply, data generation, validation, and reward allocation form a closed loop, driven by rewards and curbed by slashing, enabling network stability and continuous evolution in open environments.
Differentiated Technical Paths: Distinct “Breakthrough Points” Within a Unified Architecture
Despite architectural convergence, projects have chosen different technical moats based on their DNA:
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Algorithm Breakthrough Camp (Nous Research): Aims to solve the fundamental contradiction of distributed training (bandwidth bottleneck) at the mathematical level. Its DisTrO optimizer targets thousand-fold reductions in gradient communication, aiming to run large model training over home broadband—a “dimensional reduction strike” against physical limits.
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Systems Engineering Camp (Prime Intellect, Gensyn, Gradient): Focuses on building the next-generation “AI runtime system.” Prime Intellect’s ShardCast and Gradient’s Parallax aim to squeeze maximum efficiency from heterogeneous clusters under existing network conditions through extreme engineering.
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Market/Game Theory Camp (Bittensor, Fraction AI): Focuses on reward function design. By crafting sophisticated scoring mechanisms, they guide miners to spontaneously discover optimal strategies, accelerating intelligence emergence.
Advantages, Challenges, and Long-Term Outlook
In the RL × Web3 paradigm, systemic advantages first manifest in rewriting cost and governance structures.
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Cost Restructuring: RL post-training has infinite demand for sampling (rollout). Web3 can mobilize global long-tail compute at extremely low cost—a structural advantage centralized cloud providers cannot match.
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Sovereign Alignment: Breaks Big Tech’s monopoly on AI values (alignment). Communities can vote via tokens on “what constitutes a good answer,” democratizing AI governance.
At the same time, this system faces two structural constraints.
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Bandwidth Wall: Despite innovations like DisTrO, physical latency still limits full training of ultra-large models (70B+). Currently, Web3 AI is mostly confined to fine-tuning and inference.
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Goodhart’s Law (Reward Hacking): In highly incentivized networks, miners easily “overfit” reward rules (gaming the system) rather than improving genuine intelligence. Designing robust, anti-cheating reward functions is an eternal arms race.
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Malicious Byzantine Node Attacks: Active manipulation or poisoning of training signals can disrupt model convergence. The solution lies not just in designing anti-cheat rewards, but in building mechanisms with adversarial robustness.
The fusion of reinforcement learning and Web3 is fundamentally rewriting how “intelligence is produced, aligned, and valued.” Its evolutionary path can be summarized in three complementary directions:
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Decentralized Inference–Training Networks: Evolving from compute miners to strategy networks, outsourcing parallel, verifiable rollouts to global long-tail GPUs—short-term focus on verifiable inference markets, mid-term evolution into task-clustered RL subnets;
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Assetization of Preferences and Rewards: From annotation laborers to data equity holders. Turning high-quality feedback and Reward Models into governable, distributable data assets—upgrading from “annotation labor” to “data equity”;
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“Small but Mighty” Evolution in Vertical Domains: Nurturing small yet powerful specialized RL agents in vertically verifiable, monetizable domains like DeFi strategy execution and code generation, tightly linking strategy improvement to value capture—and potentially outperforming general-purpose closed models.
Overall, the real opportunity in reinforcement learning × Web3 is not replicating a decentralized OpenAI, but rewriting “the production relationship of intelligence”: making training execution an open compute market, turning rewards and preferences into governable on-chain assets, and redistributing the value of intelligence from platform concentration to a shared pool among trainers, aligners, and users.
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