Reinforcement Learning Meets Web3: Starting from the Reconfiguration of AI Production Relations

Under the dual drive of computing power and incentives, reinforcement learning is reshaping the fundamental logic of decentralized AI training. When this “post-training” technology meets blockchain’s economic incentive mechanisms, a paradigm-level revolution is brewing around “how intelligence is produced, aligned, and how value is distributed.”

Why has reinforcement learning suddenly become the new favorite in AI?

Last year’s emergence of DeepSeek-R1 brought a long-overlooked technical approach back into the spotlight—Reinforcement Learning (RL). Previously, the industry generally regarded RL as just a tool for value alignment, mainly used for fine-tuning model behavior. But now, it is evolving into a core technical pathway for systematically enhancing AI reasoning capabilities.

From a technical perspective, training modern Large Language Models (LLMs) involves three stages, each playing different roles in AI capability development:

Pretraining is the foundation, building the model’s “worldview” through self-supervised learning on trillions of tokens. This stage is the most expensive (cost accounts for 80%-95%), requiring thousands of H100 GPUs running in sync, and can only operate in highly centralized environments—an exclusive game for tech giants.

Instruction Fine-tuning (SFT) is the intermediate layer, injecting task-specific abilities. It is relatively low-cost (5%-15%) but still requires gradient synchronization, with limited decentralization potential.

Post-training (Post-training) is the variable. This stage includes RLHF, RLAIF, GRPO, and other reinforcement learning processes, accounting for only 5%-10% of costs, yet systematically improving reasoning quality. More importantly, it naturally supports asynchronous distributed execution—nodes do not need to hold complete weights and can dynamically join or leave. This is precisely what Web3 aims for.

The three-layer collaborative structure of reinforcement learning

To understand why RL is suitable for decentralization, we must first grasp its technical fabric.

A complete RL system consists of three roles, whose collaboration determines whether the system can operate on an open network:

Actors (Rollout Workers) handle model inference and data generation. They execute tasks based on the current policy, generating large volumes of state-action-reward trajectories. This process is highly parallel, with minimal communication between nodes, and insensitive to hardware differences. In other words, a consumer-grade GPU and an enterprise-level accelerator can work simultaneously without hindering each other.

Evaluators score the generated trajectories. They use frozen reward models or rules to evaluate each path. If task results are verifiable (e.g., math problems with standard answers), evaluation can be fully automated.

Learners (Trainers) aggregate all trajectories, perform gradient updates, and optimize policy parameters. This is the only stage requiring high bandwidth and synchronization, typically maintained centrally to ensure stable convergence.

The beauty of this triangular structure is: Rollout generation can be infinitely parallel, evaluation can be distributed, and only parameter updates require some synchronization. This flexibility is unattainable with traditional pretraining.

Evolution from RLHF to RLAIF to GRPO: the post-training paradigm

Post-training techniques are also rapidly iterating, all pointing toward a common goal—more affordable, more scalable, and better suited for decentralization:

RLHF was the initial approach, involving human preference annotations, training reward models, and optimizing policies with PPO. It is costly, slow, and hard to scale.

RLAIF replaces human annotations with AI judges, automating preference generation. OpenAI, Anthropic, DeepSeek have shifted to this approach because it reduces costs and supports rapid iteration. However, RLAIF has limitations—rewards can be gamed.

PRM (Process Reward Model) no longer only evaluates the final answer but scores each reasoning step. This is key to DeepSeek-R1 and OpenAI o1 achieving “slow thinking.” Essentially, it teaches models how to think, rather than what is right.

GRPO is DeepSeek’s latest optimizer, which, compared to PPO, does not require a Critic network (saving compute) and improves stability through intra-group advantage estimation. It performs more stably in multi-step, asynchronous environments.

The commonality across these techniques is: cost decreases with each iteration, scalability improves.

Why are Web3 and reinforcement learning a natural pair?

On the surface, Web3 is blockchain + incentive economy, and RL is an AI optimization algorithm—seemingly unrelated. But at a deeper level, both are “incentive-driven systems”:

• RL relies on reward signals to optimize strategies
• Blockchain relies on economic incentives to coordinate participants

This isomorphic nature makes the core needs of RL—large-scale heterogeneous rollout sampling, reward distribution, result verification—align perfectly with Web3’s structural advantages.

First layer match: decoupling training and inference

RL naturally splits into two phases: Rollout (data generation) and Update (weight optimization). Rollout involves sparse communication and can be fully parallelized, ideally on a global network of consumer GPUs; Update requires high-bandwidth centralized nodes. This “asynchronous, lightweight synchronization” architecture is exactly what decentralized networks are built for.

Second layer match: verifiability

In open networks, honesty cannot be assumed; cryptography or logical verification is necessary. Fortunately, many RL task outcomes are verifiable: code compiles, math answers are correct, games have winners. This enables “Proof-of-Learning”—verifying whether nodes truly performed inference rather than just faking results.

Third layer match: programmable incentives

Web3 tokens can directly reward preference feedback generators, rollout contributors, and verifiers. Staking and slashing mechanisms further enforce honest participation. This is much more transparent and cost-effective than traditional crowdsourcing.

Six representative projects in decentralized reinforcement learning

Currently, multiple teams are experimenting at this intersection. Their approaches vary, but the underlying logic is surprisingly consistent.

Prime Intellect: Asynchronous distributed proof of concept

Prime Intellect aims to build a global open compute market, centered on the prime-rl framework—a reinforcement learning engine designed specifically for large-scale asynchronous decentralized environments.

Traditional PPO requires all nodes to synchronize, with slow nodes dragging down the whole. prime-rl breaks this limitation: actors and learners are fully decoupled, actors can join or leave at any time without waiting for batch completion.

Technically, prime-rl integrates vLLM for high-throughput inference, FSDP2 for parameter sharding, and MoE for sparse activation. This enables training of models with hundreds of billions of parameters on heterogeneous GPU clusters.

Prime Intellect’s models in the INTELLECT series demonstrate the feasibility: INTELLECT-1 (10B) achieves 98% utilization across networks spanning three continents with low communication costs (<2%); INTELLECT-2 (32B) first validates permissionless RL; INTELLECT-3 (106B MoE) trains flagship models on consumer GPUs (AIME accuracy 90.8%, GPQA 74.4%).

These iterations prove: decentralized RL is moving from concept to reality.

Gensyn: RL Swarm and SAPO framework

Gensyn’s approach is more aggressive—distributing not just compute but the entire collaborative learning process.

Its core innovation is RL Swarm and SAPO (Swarm Sampling Policy Optimization). RL Swarm reimagines RL as a P2P “generate-evaluate-update” loop:

• Solvers generate inference trajectories
• Proposers dynamically create tasks
• Evaluators score

No central coordination needed; they form a self-consistent learning system. SAPO is an optimization algorithm designed for this fully asynchronous environment—it does not share gradients, only trajectory samples, with minimal communication overhead.

Gensyn’s philosophy: The real scalability of RL lies not in parameter updates, but in large-scale, diverse rollout exploration. If so, why not decentralize this part entirely?

Nous Research: Closed-loop system with verifiable rewards

Nous Research has built a more complete ecosystem, including Hermes models, Atropos verification environment, DisTrO distributed training optimizer, and Psyche decentralized GPU network.

Atropos is particularly innovative. It’s not just an RL environment but a “verifiable reward layer.” For verifiable tasks like math or code, Atropos directly verifies correctness and generates deterministic rewards. For uncertain results, it provides standardized RL environment interfaces.

More importantly, in the decentralized training network Psyche, Atropos acts as a “referee”—verifying whether miners truly improved the policy. This directly addresses the biggest trust issue in distributed RL.

In their system, RL is not an isolated training phase but a core protocol connecting data, environment, models, and infrastructure. Hermes is evolving into a “living system” capable of continuous self-improvement on open compute networks.

Gradient Network: Echo framework and dual-group architecture

Gradient’s Echo framework adopts a “Inference Group + Training Group” dual-group architecture, each operating independently. The inference group consists of consumer GPUs and edge devices, focusing on high-throughput trajectory generation; the training group handles gradient updates and parameter synchronization.

Echo offers two synchronization modes: sequential (ensures fresh trajectories but may waste compute) and asynchronous (maximizes device utilization but tolerates more delay). This flexibility allows adaptation to various network conditions.

Gradient’s entire tech stack integrates distributed inference (Parallax), RL training (Echo), P2P networking (Lattica), and verification (VeriLLM). It may be the most complete “open intelligence protocol stack” to date.

Bittensor ecosystem’s Grail subnet

Bittensor, via its unique Yuma consensus, constructs a vast, sparse, non-stationary reward function network. Covenant AI has built a full pipeline from pretraining to RL post-training within this ecosystem.

Grail subnet is an “verifiable inference layer” for RL post-training. Its innovation is cryptographically proving the authenticity of each RL rollout:

1. Using drand random beacons to generate unpredictable challenge tasks (SAT, GSM8K, etc.), preventing precomputation cheating
2. Sampling with PRF indices and sketch commitments, enabling verifiers to efficiently check inference processes at low cost
3. Binding inference to model fingerprinting, ensuring model replacements are immediately detectable

Public experiments show Grail improves Qwen2.5-1.5B’s math accuracy from 12.7% to 47.6%, preventing cheating and significantly enhancing model capability.

Fraction AI: Competition-driven reinforcement learning

If previous projects focus on “how to decentralize training,” Fraction AI emphasizes “how to drive learning through competition.”

Fraction AI replaces static rewards in RLHF with a dynamic competitive environment. Agents compete across different task spaces, with relative rankings and AI judge scores forming real-time rewards. This transforms alignment into a continuous multi-agent game system.

Architecturally, Fraction decomposes into four modules: lightweight Agents (fine-tuned via QLoRA), isolated task spaces, decentralized AI Judges, and Proof-of-Learning verification layer.

At its core, Fraction is an “evolution engine for human-AI collaboration”: users guide via prompt engineering, while agents autonomously generate vast high-quality preference data through micro-competition. In this mode, data labeling is no longer labor cost but a business cycle enabled by trustless fine-tuning.

Technical comparison of the six projects

The three structural advantages of reinforcement learning × Web3

Although the projects approach differently, when RL is combined with Web3, the underlying architecture converges into a highly consistent paradigm: decoupling - verification - incentives.

First: decoupling inference and training becomes standard

Sparse, highly parallel rollout outsourcing to global consumer GPU networks; high-bandwidth parameter updates centralized in a few training nodes. From Prime Intellect’s asynchronous Actor-Learner to Gradient’s dual-group, and Gensyn’s fully decentralized Swarm—this pattern is becoming the norm.

Second: verification as infrastructure

In permissionless networks, honesty cannot be assumed; cryptography and mechanism design are essential. Gensyn’s PoL, Prime Intellect’s TopLoc, Nous’s Atropos, Grail’s cryptographic challenges—all address the same core question: how to establish trust among strangers. These verification layers will evolve into universal “trusted computing infrastructure.”

Third: tokenization incentives as a natural choice

Providing compute, generating data, verifying, and rewarding form a complete closed loop. Rewards incentivize participation; slashing enforces honesty. In open environments, this maintains stability. Compared to traditional crowdsourcing—manual review with fixed pay—this mechanism is several orders of magnitude more efficient and scalable.

The three major challenges ahead

Behind the promising vision lie harsh realities. The path of RL × Web3 still faces three major mountains:

First: bandwidth barrier

Despite innovations like DisTrO for gradient compression, physical latency still limits full training of very large models (70B+). Currently, Web3 AI is mostly limited to fine-tuning and inference; full end-to-end training comparable to centralized cloud providers remains out of reach.

Second: adversarial fragility of reward functions

This reflects Goodhart’s law digitally. In highly incentivized networks, miners will do their best to “overfit” reward rules. Superficially, models seem to improve, but in reality, they may just be gaming the scores. Designing robust, hard-to-gamify reward functions is an eternal game.

Third: Byzantine node poisoning attacks

Malicious nodes can actively manipulate training signals to sabotage the entire network’s convergence. This cannot be solved simply by better reward functions; it requires mechanisms to build adversarial robustness.

Three possible evolution paths

Despite challenges, the evolution of RL × Web3 is becoming clearer. Future development may follow three complementary routes:

Path 1: layered evolution of decentralized inference networks

From simple compute miners to task-clustered RL subnets. Short-term focus on verifiable inference markets (code, math), mid-term expanding to multi-step reasoning and policy optimization, long-term forming an open infrastructure covering inference, training, and alignment. Prime Intellect and Gensyn are heading this way.

Path 2: assetization of preferences and rewards

From low-value “labeling labor” to “data equity.” Achieving high-quality feedback and reward model governance, turning them into on-chain tradable assets. Fraction AI’s competitive framework already points in this direction—users are no longer passive labelers but active participants in ongoing game dynamics.

Path 3: vertical “small and beautiful” AI agents

In verifiable, quantifiable vertical scenarios, develop small but powerful RL agents—e.g., DeFi strategies, code auditing, mathematical proofs. In these domains, strategy improvement and value capture are directly linked, potentially outperforming general-purpose, closed-source large models.

The ultimate imagination space

The real opportunity of RL × Web3 is not merely copying a decentralized version of OpenAI or DeepSeek, but fundamentally rewriting the production relations of “how intelligence is produced, aligned, and how value is distributed.”

In centralized models, AI capabilities are proprietary to tech giants; alignment is a black box; value is monopolized by platforms. In the Web3 paradigm, training and inference become open compute markets, rewards and preferences are on-chain governance assets, and the benefits of intelligence are redistributed among contributors, verifiers, and users.

This is not just a technical issue but a power reconfiguration—“who decides AI’s values,” “who benefits from AI progress.” When this transformation completes, we may look back and realize: the integration of RL and Web3 not only changes how AI is produced but also redefines the social nature of the AI revolution itself.