From computing power to intelligence, a decentralized AI investment map driven by reinforcement learning

Artificial intelligence is transitioning from statistical learning primarily based on “pattern fitting” to a capabilities system centered on “structured reasoning,” with the importance of post-training rapidly increasing. The emergence of DeepSeek-R1 marks a paradigm shift for reinforcement learning in the era of large models, leading to industry consensus: pretraining builds the general ability foundation of models, while reinforcement learning is no longer just a tool for value alignment but has proven capable of systematically improving the quality of reasoning chains and complex decision-making abilities, gradually evolving into a technical path for continuously enhancing intelligence.

Meanwhile, Web3 is reconstructing the production relationship of AI through decentralized compute networks and cryptographic incentive systems. Reinforcement learning’s structural needs for rollout sampling, reward signals, and verifiable training naturally align with blockchain’s compute collaboration, incentive distribution, and verifiable execution. This report systematically dissects AI training paradigms and reinforcement learning principles, demonstrates the structural advantages of reinforcement learning × Web3, and analyzes projects such as Prime Intellect, Gensyn, Nous Research, Gradient, Grail, and Fraction AI.

Three Stages of AI Training: Pretraining, Instruction Fine-tuning, and Post-training Alignment

The full lifecycle of modern large language model (LLM) training is typically divided into three core stages: pretraining (Pre-training), supervised fine-tuning (SFT), and post-training (Post-training/RL). Each serves functions of “building world models—injecting task capabilities—shaping reasoning and values,” with their computational structures, data requirements, and validation difficulties determining the degree of decentralization matching.

· Pretraining (Pre-training): Constructs the model’s language statistical structure and cross-modal world model through large-scale self-supervised learning, serving as the foundation of LLM capabilities. This stage requires training on trillions of tokens in a globally synchronized manner, relying on thousands to tens of thousands of H100 homogeneous clusters, with costs accounting for 80–95%. It is highly sensitive to bandwidth and data copyright, thus must be completed in a highly centralized environment.

· Fine-tuning (Supervised Fine-tuning): Injects task capabilities and instruction formats, involving smaller data volumes and accounting for about 5–15% of costs. Fine-tuning can be full-parameter or parameter-efficient (PEFT), with LoRA, Q-LoRA, and Adapters being mainstream in industry. However, it still requires synchronized gradients, limiting decentralization potential.

· Post-training (Post-training): Comprises multiple iterative sub-stages that determine the model’s reasoning ability, values, and safety boundaries. Methods include reinforcement learning systems (RLHF, RLAIF, GRPO), preference optimization without RL (DPO), and process reward models (PRM). This stage involves lower data volume and costs (5–10%), mainly focusing on rollout and policy updates. It naturally supports asynchronous and distributed execution, with nodes not needing to hold full weights, and can incorporate verifiable computation and on-chain incentives to form an open decentralized training network—most suitable for Web3.

Reinforcement Learning Technology Panorama: Architecture, Framework, and Applications

System Architecture and Core Components of Reinforcement Learning

Reinforcement learning (RL) drives models to improve decision-making through “environment interaction—reward feedback—policy update,” with its core structure forming a feedback loop of states, actions, rewards, and policies. A complete RL system typically includes three component types: Policy (policy network), Rollout (experience sampling), and Learner (policy updater). The policy interacts with the environment to generate trajectories; the learner updates the policy based on reward signals, forming a continuous iterative learning process:

1. Policy Network (Policy): Generates actions from environment states, serving as the decision core. During training, it requires centralized backpropagation for consistency; during inference, it can be distributed across nodes for parallel execution.

2. Experience Sampling (Rollout): Nodes execute environment interactions based on the policy, generating state-action-reward trajectories. This process is highly parallel, with minimal communication, and insensitive to hardware differences—ideal for decentralized scaling.

3. Learner (Trainer): Aggregates all rollout trajectories and performs policy gradient updates. It demands the highest compute and bandwidth, often maintained centrally or lightly centralized to ensure stable convergence.

Reinforcement Learning Stage Framework (RLHF → RLAIF → PRM → GRPO)

Reinforcement learning generally proceeds through five stages, with the overall process as follows:

Data Generation Stage (Policy Exploration)

Under given prompts, the policy model πθ generates multiple candidate reasoning chains or complete trajectories, providing samples for preference evaluation and reward modeling, determining the breadth of policy exploration.

Preference Feedback Stage (RLHF / RLAIF)

· RLHF (Reinforcement Learning from Human Feedback): Uses multiple candidate answers, human preference annotations, trains a reward model (RM), and employs PPO to optimize the policy, making outputs more aligned with human values. It is a key step from GPT-3.5 to GPT-4.

· RLAIF (Reinforcement Learning from AI Feedback): Replaces human annotations with AI judges or constitutional rules, enabling automated preference acquisition, significantly reducing costs and scaling. It has become mainstream in alignment for Anthropic, OpenAI, DeepSeek, etc.

Reward Modeling Stage (Reward Modeling)

Preference data trains the reward model to map outputs to rewards. RM teaches the model “what is the correct answer,” while PRM (Process Reward Model) teaches “how to reason correctly.”

· RM (Reward Model): Evaluates the quality of final answers, scoring outputs.

· PRM (Process Reward Model): Scores each reasoning step, token, or logical segment, key in OpenAI o1 and DeepSeek-R1, essentially teaching the model “how to think.”

Reward Verification Stage (RLVR / Reward Verifiability)

In the process of generating and using reward signals, introduces “verifiable constraints” to ensure rewards come from reproducible rules, facts, or consensus, reducing reward hacking and bias, and improving auditability and scalability in open environments.

Policy Optimization Stage (Policy Optimization)

Updates policy parameters θ under guidance from reward models to obtain stronger reasoning, higher safety, and more stable behaviors. Main optimization methods include:

· PPO (Proximal Policy Optimization): The traditional optimizer for RLHF, known for stability but often slow convergence and less stability in complex reasoning tasks.

· GRPO (Group Relative Policy Optimization): The core innovation of DeepSeek-R1, models advantage distributions within candidate answer groups to estimate expected value rather than simple ranking. It retains reward magnitude information, better suited for reasoning chain optimization, with more stable training, regarded as an important RL framework for deep reasoning scenarios after PPO.

· DPO (Direct Preference Optimization): A post-training method that does not generate trajectories or build reward models but directly optimizes on preference pairs, low cost, stable effect. Widely used in open-source models like Llama, Gemma for alignment but does not improve reasoning ability.

New Policy Deployment (Deployment of the New Policy)

The optimized model exhibits: enhanced reasoning chain generation (System-2 reasoning), behavior more aligned with human or AI preferences, lower hallucination rates, and higher safety. Through continuous iteration, the model learns preferences, optimizes processes, and improves decision quality, forming a closed loop.

Industrial Applications of Reinforcement Learning: Five Major Categories

RL has evolved from early game intelligence to a core decision-making framework across industries. Its application scenarios, based on maturity and industry adoption, can be summarized into five categories, each driving key breakthroughs:

· Game & Strategy: The earliest verified RL applications, demonstrated in environments like AlphaGo, AlphaZero, AlphaStar, OpenAI Five (“perfect information + explicit rewards”), showcasing decision intelligence comparable or superior to human experts, laying the foundation for modern RL algorithms.

· Embodied AI (Robotics & Physical Agents): RL enables robots to learn control, motion, and cross-modal tasks (e.g., RT-2, RT-X) through continuous control, dynamics modeling, and environment interaction, rapidly moving toward industrialization—key for real-world robot deployment.

· Digital Reasoning (LLM System-2): RL + PRM drive large models from “language imitation” toward “structured reasoning,” with results including DeepSeek-R1, OpenAI o1/o3, Anthropic Claude, and AlphaGeometry. Essentially, reward optimization at the reasoning chain level, not just final answer evaluation.

· Scientific Discovery & Mathematical Optimization: RL finds optimal structures or strategies in unlabeled, complex reward, and large search spaces, achieving breakthroughs like AlphaTensor, AlphaDev, Fusion RL, demonstrating exploration beyond human intuition.

· Economic Decision-Making & Trading Systems: RL is used for strategy optimization, high-dimensional risk control, and adaptive trading system generation, surpassing traditional quantitative models in uncertain environments, key to intelligent finance.

Natural Fit of Reinforcement Learning and Web3

The high compatibility between RL and Web3 stems from their shared nature as “incentive-driven systems.” RL relies on reward signals to optimize policies, while blockchain coordinates participant behavior through economic incentives, making their mechanisms inherently aligned. RL’s core needs—large-scale heterogeneous rollout, reward distribution, and verifiable computation—are precisely Web3’s structural advantages.

Decoupling Reasoning and Training

The RL training process can be clearly split into two stages:

· Rollout Exploration (: The model generates large amounts of data based on the current policy, a compute-intensive but communication-light task. It does not require frequent inter-node communication, suitable for parallel generation on globally distributed consumer-grade GPUs.

· Parameter Update ): Based on collected data, model weights are updated on high-bandwidth centralized nodes.

“Decoupled inference and training” naturally fits the decentralized, heterogeneous compute architecture: rollout can be outsourced to open networks and settled via tokens based on contribution, while model updates remain centralized to ensure stability.

Verifiability (: Verifiable computation methods like Zero-Knowledge proofs and Proof-of-Learning provide means to verify whether nodes truly performed inference, solving honesty issues in open networks. For deterministic tasks like code or mathematical reasoning, verifiers only need to check answers, greatly enhancing trustworthiness of decentralized RL systems.

Incentive Layer: Token-Based Feedback Production

Web3’s token mechanisms can directly reward contributors of preferences and feedback in RLHF/RLAIF, making preference data generation transparent, verifiable, and permissionless. Staking and slashing further constrain feedback quality, creating a more efficient and aligned feedback market than traditional crowdsourcing.

Multi-Agent Reinforcement Learning (MARL) Potential

Blockchain is inherently a transparent, continuously evolving multi-agent environment, with accounts, contracts, and agents constantly adjusting strategies driven by incentives. Its open state, verifiable execution, and programmable incentives provide principled advantages for developing large-scale MARL experiments. Although still early, its transparent state, verifiable execution, and incentive programmability make it a promising foundation for future MARL development.

Analysis of Classic Web3 + Reinforcement Learning Projects

Based on the above framework, we briefly analyze some of the most representative projects in the ecosystem:

Prime Intellect: Asynchronous Reinforcement Learning Paradigm prime-rl

Prime Intellect aims to build a global open compute market, lowering training barriers, promoting collaborative decentralized training, and developing a complete open-source superintelligence stack. Its system includes: Prime Compute (unified cloud/distributed compute environment), INTELLECT model family (10B–100B+ parameters), open RL environment hub, and large-scale synthetic data engine (SYNTHETIC-1/2).

The core infrastructure component, prime-rl, is designed specifically for asynchronous distributed environments and is highly relevant to RL. It includes breakthroughs like bandwidth-efficient OpenDiLoCo communication protocol and TopLoc verification for computational integrity.

Prime Intellect Core Infrastructure Components Overview

Technical Foundation: prime-rl Asynchronous RL Framework

prime-rl is the core training engine of Prime Intellect, designed for large-scale asynchronous decentralized environments, decoupling Actor and Learner for high throughput inference and stable updates. Actors )Rollout Workers( and the Learner )Trainer( no longer synchronize blocking; nodes can join or leave at will, simply pulling the latest policy and uploading generated data:

· Actor )Rollout Workers(: Responsible for model inference and data generation. Prime Intellect innovatively integrates vLLM inference engine at the actor end. PagedAttention and continuous batching enable high-throughput trajectory generation.

· Learner )Trainer(: Performs policy optimization. It asynchronously pulls data from shared experience buffers for gradient updates, without waiting for all actors.

· Orchestrator ): Manages model weights and data flow.

Key Innovations of prime-rl

· True Asynchrony: Abandons traditional PPO synchronization, does not wait for slow nodes, no batch alignment, allowing any number and performance of GPUs to join, enabling decentralized RL.

· Deep integration of FSDP2 and MoE: Using FSDP2 parameter sharding and sparse MoE activation, enabling efficient training of models with hundreds of billions of parameters, with actors running only active experts, greatly reducing memory and inference costs.

· GRPO+ (Group Relative Policy Optimization): Eliminates the critic network, reducing computation and memory; naturally suited for asynchronous environments. Its stabilization mechanisms ensure reliable convergence under high latency.

INTELLECT Model Family: Sign of Decentralized RL Maturity

· INTELLECT-1 (10B, October 2024): Demonstrated efficient training of OpenDiLoCo across three continents with communication <2% and 98% compute utilization, breaking physical barriers of cross-region training.

· INTELLECT-2 (32B, April 2025): The first permissionless RL model, validating stable convergence of prime-rl and GRPO+ in multi-step delayed, asynchronous environments, enabling global open compute participation.

· INTELLECT-3 (106B MoE, November 2025): Uses sparse architecture activating only 12B parameters, trained on 512×H200, achieving flagship inference performance (AIME 90.8%, GPQA 74.4%, MMLU-Pro 81.9%), approaching or surpassing larger centralized closed models.

Additionally, Prime Intellect has built supporting infrastructure: OpenDiLoCo reduces cross-region communication by orders of magnitude via sparse communication and weight quantization, maintaining 98% utilization for INTELLECT-1 across continents; TopLoc + Verifiers form a decentralized trusted execution layer, ensuring authenticity of inference and reward data; SYNTHETIC engine produces large-scale high-quality reasoning chains, enabling efficient operation of 671B models on consumer GPU clusters. These components provide critical engineering foundations for decentralized RL data generation, verification, and inference throughput. The success of the entire stack demonstrates that decentralized training can produce world-class models, marking a transition from concept to practical deployment.

Gensyn: Core Reinforcement Learning Stack RL Swarm and SAPO

Gensyn aims to aggregate global idle compute into an open, trustless, infinitely scalable AI training infrastructure. Its core includes cross-device standardized execution layer, peer-to-peer coordination network, and trustless task verification system, with smart contracts automating task and reward distribution. Centered on RL, Gensyn introduces RL Swarm, SAPO, and SkipPipe, decoupling generation, evaluation, and update phases, leveraging a “swarm” of heterogeneous GPUs for collective evolution. The final deliverable is not just compute but verifiable intelligence (Verifiable Intelligence).

Gensyn RL Application Stack

RL Swarm: Decentralized Cooperative RL Engine

RL Swarm demonstrates a new cooperative mode. It’s not just task distribution but a decentralized “generation—evaluation—update” loop mimicking human social learning, with infinite cycles:

· Solvers (Executors): Handle local model inference and rollout generation, nodes are heterogeneous. Gensyn integrates high-throughput inference engines (e.g., CodeZero) locally, capable of outputting full trajectories.

· Proposers (Task Generators): Dynamically generate tasks (math, coding, etc.), supporting task diversity and curriculum-like adaptive difficulty.

· Evaluators: Use frozen “judge models” or rules to assess local rollouts, generating local reward signals. The evaluation process is auditable, reducing malicious behavior.

These three form a P2P RL organization, enabling large-scale collaboration without centralized scheduling.

SAPO: Decentralized Policy Optimization Algorithm

SAPO (Swarm Sampling Policy Optimization) centers on “sharing rollouts and filtering samples without gradients,” maintaining stability in environments with no central coordination and significant node delays. It uses large-scale decentralized rollout sampling, treating received rollouts as locally generated, ensuring convergence. Compared to PPO (which relies on critic networks and is computationally expensive) or GRPO (which estimates advantage within groups), SAPO uses minimal bandwidth, allowing consumer-grade GPUs to participate effectively in large-scale RL.

Through RL Swarm and SAPO, Gensyn demonstrates that RL—especially post-training RLVR—is naturally suited for decentralized architectures—since it relies more on large-scale, diverse exploration (rollouts) than on frequent parameter synchronization. Coupled with PoL and Verde verification systems, Gensyn offers an alternative path for trillion-parameter models, built on a self-evolving superintelligent network 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—form a continuous closed-loop AI evolution system. Unlike traditional linear “pretraining—post-training—inference,” Nous employs RL techniques like DPO, GRPO, rejection sampling, integrating data generation, verification, learning, and reasoning into a continuous feedback loop, creating a self-improving AI ecosystem.

Nous Research Components Overview

Model Layer: Hermes and Reasoning Capabilities

Hermes series are the main model interfaces for users, illustrating the industry’s migration from traditional SFT/DPO alignment toward reasoning RL:

· Hermes 1–3: Instruction alignment and early capabilities: rely on low-cost DPO for robust instruction alignment; Hermes 3 incorporates synthetic data and the first Atropos verification.

· Hermes 4 / DeepHermes: embed System-2 slow thinking into weights via chain-of-thought, improve math and coding performance with Test-Time Scaling, and build high-purity reasoning data via rejection sampling + Atropos verification.

· DeepHermes replaces PPO with GRPO, enabling reasoning RL on Psyche’s decentralized GPU network, laying groundwork for scalable open-source reasoning RL.

Atropos: Verifiable Reward-Driven RL Environment

Atropos encapsulates prompts, tool calls, code execution, and multi-turn interactions into a standard RL environment, directly verifying correctness, providing deterministic reward signals, replacing costly human annotations. Crucially, in the decentralized Psyche network, Atropos acts as a “judge” to verify whether nodes truly improved policies, supporting verifiable Proof-of-Learning, fundamentally solving reward trust issues in distributed RL.

DisTrO and Psyche: Optimization Layer for Decentralized RL

Traditional RL training (RLHF/RLAIF) depends on centralized high-bandwidth clusters, a barrier for open-source. DisTrO decouples momentum and compresses gradients, reducing communication costs by orders of magnitude, enabling training over internet bandwidth. Psyche deploys this mechanism on-chain, allowing nodes to perform inference, verification, reward evaluation, and weight updates locally, forming a complete RL loop.

In this architecture, Atropos verifies reasoning chains; DisTrO compresses training communication; Psyche runs RL cycles; World Sim provides complex environments; Forge collects real inference data; Hermes encodes all learning into weights. Reinforcement learning becomes not just a training phase but a core protocol connecting data, environment, models, and infrastructure, making Hermes a living system capable of continuous self-improvement over open compute networks.

Gradient Network: Reinforcement Learning Architecture Echo

Gradient Network aims to reconstruct AI computation paradigms via an “Open Intelligence Stack.” Its stack comprises evolving, heterogeneous core protocols—from Parallax (distributed inference), Echo (decentralized RL training), Lattica (P2P network), to modules like SEDM, Massgen, Symphony, CUAHarm (memory, collaboration, security), VeriLLM (trust verification), Mirage (high-fidelity simulation)—forming a continuously evolving decentralized intelligent infrastructure.

Echo—Reinforcement Learning Training Architecture

Echo is Gradient’s RL framework, designed to decouple training, inference, and data (reward) pathways, enabling independent scaling and scheduling of rollout generation, policy optimization, and reward evaluation across heterogeneous nodes. It operates collaboratively over inference and training nodes, maintaining training stability via lightweight synchronization, alleviating issues like SPMD failure and GPU utilization bottlenecks common in DeepSpeed RLHF / VERL.

Echo employs a “dual-group” architecture for maximum compute utilization:

· Maximize sampling throughput: Inference Swarm, composed of consumer GPUs and edge devices, uses Parallax pipeline-parallelism for high-throughput trajectory sampling.

· Maximize gradient compute: Training Swarm, composed of centralized or globally distributed consumer GPUs, handles gradient updates, parameter synchronization, and LoRA fine-tuning.

To ensure policy and data consistency, Echo offers sequential (Pull) and asynchronous (Push–Pull) synchronization protocols:

· Sequential Pull: Prioritizes accuracy; training node forces inference nodes to refresh models before pulling new trajectories, suitable for highly sensitive tasks.

· Asynchronous Push–Pull: Inference nodes continuously generate versioned trajectories; training nodes consume at their pace; orchestrator monitors version drift and triggers weight refreshes, maximizing device utilization.

At the low level, Echo builds on Parallax (heterogeneous inference in low-bandwidth environments) and lightweight distributed training components (e.g., on VERL(), relying on LoRA to reduce cross-node synchronization costs, enabling stable RL over global heterogeneous networks.

Grail: Reinforcement Learning in the Bittensor Ecosystem

Bittensor’s unique Yuma consensus mechanism constructs a vast, sparse, non-stationary reward function network.

Within Bittensor, Covenant AI uses SN3 Templar, SN39 Basilica, and SN81 Grail to build a vertical pipeline from pretraining to RL post-training. SN3 Templar handles base model pretraining; SN39 Basilica provides distributed compute markets; SN81 Grail acts as a “verifiable inference layer” for RLHF/RLAIF, completing the closed-loop optimization from base models to aligned policies.

GRAIL aims to cryptographically prove the authenticity of each RL rollout and bind it to model identity, ensuring RLHF can be securely executed in trustless environments. The protocol establishes a trusted chain via three mechanisms:

1. Deterministic challenge generation: Using drand randomness beacon and block hashes to produce unpredictable but reproducible challenges (e.g., SAT, GSM8K), preventing precomputation cheating.

2. Using PRF-based sampling and sketch commitments, verifiers can efficiently check token-level log probabilities and inference chains, confirming rollouts are generated by declared models.

3. Model identity binding: Embeds inference process and model weight fingerprints with structural signatures on token distributions, ensuring any model replacement or replay is immediately detected. This provides a foundation for the authenticity of RL rollouts.

Based on this, Grail subnets implement a GRPO-style verifiable post-training process: miners generate multiple reasoning paths for the same problem; verifiers score based on correctness, reasoning chain quality, and SAT satisfaction, then write normalized results on-chain as TAO weights. Experiments show this framework improves Qwen2.5-1.5B’s MATH accuracy from 12.7% to 47.6%, demonstrating robustness against cheating and significant capability enhancement. Grail is a trust and execution cornerstone for decentralized RLVR/RLAIF in Covenant AI’s stack, not yet fully on mainnet.

Fraction AI: Competitive Reinforcement Learning RLFC

Fraction AI’s architecture centers on Reinforcement Learning from Competition (RLFC) and gamified data annotation, replacing traditional RLHF’s static rewards and human annotations with open, dynamic competition environments. Agents compete within different Spaces; their relative rankings and AI judge scores form real-time rewards, transforming alignment into a continuous multi-agent game.

Key differences between RLHF and Fraction AI’s RLFC:

RLFC’s core value: rewards come not from a single model but from evolving opponents and evaluators, preventing reward exploitation and encouraging strategy diversity to avoid local optima. The structure of Spaces determines the game nature (zero-sum or positive-sum), fostering emergent complex behaviors through adversarial and cooperative interactions.

System architecture decomposes training into four key components:

· Agents: lightweight policy units based on open-source LLMs, extended via QLoRA for low-cost updates.

· Spaces: isolated task domains where agents pay to participate and earn rewards based on wins/losses.

· AI Judges: real-time reward layer built with RLAIF, providing scalable, decentralized evaluation.

· Proof-of-Learning: binds policy updates to specific competition outcomes, ensuring verifiability and anti-cheating.

Fundamentally, Fraction AI constructs an “evolution engine” of human-AI collaboration: users act as “meta-optimizers” )Meta-optimizer( via prompt engineering and hyperparameter tuning; agents autonomously generate vast high-quality preference data pairs )Preference Pairs) through microscopic competition. This approach enables “trustless fine-tuning,” creating a closed-loop business model.

Comparison of Reinforcement Learning Web3 Projects

Summary and Outlook: Paths and Opportunities for Reinforcement Learning × Web3

From the above analysis, we observe that although different teams focus on algorithms, engineering, or market, the underlying architecture of RL + Web3 converges to a highly consistent “decouple-verify-incentivize” paradigm. This is not only a technical coincidence but also an inevitable adaptation of decentralized networks to the unique properties of reinforcement learning.

Common Features of Reinforcement Learning Architecture: Solving Core Physical and Trust Constraints

1. Decoupling Rollouts & Learning (Decoupling of Rollouts & Learning): The default compute topology involves sparse, parallel rollout outsourcing to global consumer GPUs, with high-bandwidth parameter updates centralized in a few training nodes—exemplified by Prime Intellect’s asynchronous Actor–Learner and Gradient’s dual-group architecture.

2. Verification-Driven Trust Layer (Verification-Driven Trust): In permissionless networks, computational authenticity must be enforced via mathematical and mechanism design, exemplified by Gensyn’s PoL, Prime Intellect’s TOPLOC, and Grail’s cryptographic verification.

3. Tokenized Incentive Loop (Tokenized Incentive Loop): The supply of compute, data generation, verification, and reward distribution form a closed market, driven by incentives, with mechanisms like slashing to prevent cheating, maintaining stability and continuous evolution in open environments.

Different “Breakthrough Points” under the Same Architecture

Despite architectural convergence, projects choose different technological “moats” based on their core strengths:

· Algorithmic Breakthroughs (Nous Research): Aiming to fundamentally solve the physical bottleneck of distributed training (bandwidth). Its DisTrO optimizer compresses gradient communication by thousands of times, targeting household broadband to train large models—an “attack” on physical limits.

· System Engineering (Prime Intellect, Gensyn, Gradient): Focused on building next-generation “AI runtime systems.” Prime Intellect’s ShardCast and Gradient’s Parallax aim to maximize heterogeneous cluster efficiency through engineering.

· Market & Game Theory (Bittensor, Fraction AI): Focused on reward function design. By crafting clever scoring mechanisms, they guide miners to find optimal strategies, accelerating emergent intelligence.

Advantages, Challenges, and Future Outlook

In the RL + Web3 paradigm, the systemic advantages primarily manifest in cost and governance restructuring:

· Cost Reconfiguration: RL post-training (rollout) demands are infinite; Web3 can mobilize global long-tail compute at extremely low costs, a competitive edge over centralized cloud providers.

· Sovereign Alignment (Sovereign Alignment): Breaking the monopoly of big tech over AI values, communities can use token voting to define “what is a good answer,” democratizing AI governance.

However, the system also faces two fundamental constraints:

· Bandwidth Wall (Bandwidth Wall): Despite innovations like DisTrO, physical latency limits the training of very large models (70B+). Currently, Web3 AI is more limited to fine-tuning and inference.

· Goodhart’s Law (Reward Hacking): In highly incentivized networks, miners tend to “overfit” reward rules rather than genuinely improve intelligence. Designing robust, cheat-resistant reward functions remains an eternal game.

· Malicious Byzantine Nodes (BYZANTINE worker): Active manipulation and poisoning of training signals can disrupt convergence. The core challenge is to build mechanisms with adversarial robustness, not just anti-cheating reward functions.

The integration of RL and Web3 fundamentally rewrites the “how intelligence is produced, aligned, and distributed.” Its evolution can be summarized into three complementary directions:

1. Decentralized Rollout & Training Networks: Outsourcing parallel, verifiable rollout to global long-tail GPUs, initially focusing on verifiable inference markets, evolving into task-clustered RL subnetworks.

2. Assetization of Preferences & Rewards: Transforming annotations and reward signals into governance and distributable data assets, elevating high-quality feedback from labor to data equity.

3. Vertical “Small and Beautiful” Evolution: Developing small, specialized RL agents in verifiable, quantifiable scenarios, such as DeFi strategies or code generation, directly tying policy improvement and value capture, with potential to outperform general closed-source models.

Overall, the real opportunity of RL × Web3 is not merely replicating a decentralized OpenAI but rewriting the “mechanism of producing, aligning, and distributing intelligence”: enabling training to be an open compute market, rewards and preferences to be on-chain governance assets, and redistributing the value generated by intelligence among trainers, aligners, and users. ($ALLO