AL INVESTMENT MAP

Author:Jacob ZhaoIOSG

It's the smartest thing in the world“Model alignment”Mainly statistical learning towards“Structural reasoning”As a core competency systemPost-trainingThe importance is rising rapidly。DeepSeek-R1It's a signEnhanced learningA paradigm shift in the age of large models has led to an industry consensus:Pre-trainingThe general capability base for building modelsEnhanced learningInstead of being a value-matching tool, it has been shown to be able to systematically upgrade the quality of the reasoning chain and complex decision-making capabilities and is gradually evolving into a technological path of continuous upgrading of intelligence levels。

In the meantimeWeb3The restructuring of AI ' s production relationship with the encryption incentive system is being re-engineered through decentrization of the algorithm network, and the need for enhanced learning on rollout sampling, reward signals, and verifiable training is well aligned with the calculation of block chains, the distribution of incentives and the validation of natural synergies. The study will decompose AI training paradigms and enhanced learning techniques, demonstrate the structural advantages of enhanced learning x Web3 and analyse projects such as Prime Intelect, Gensyn, Nous Research, Gradient, Grail and Fracing AI。

I. AI THREE STAGES OF TRAINING: PRE-TRAINING, CALIBRATION OF COMMAND AND POST-TRAINING ALIGNMENT

Modern Large Language Model (HLM)LLMThe entire training life cycle is usually divided into three core stages: pre-training (Pre-training), oversight fine-tuning (Pre-training)SFT(d) Post-training/RL. The three perform the functions of “building a world model — mission capability — shaping reasoning and values”, whose computational structure, data requirements and validation difficulties determine the degree of decentrization。

Pre-trainingBy massiveSelf-supervised learning (Self-supervised Learning)MODELLING LINGUISTIC STATISTICAL STRUCTURES AND CROSS-MODULAR WORLD MODELS ARE FUNDAMENTAL TO LLM CAPABILITIES. THIS PHASE, WHICH INVOLVES TRAINING IN A GLOBAL AND SYNCHRONIZED MANNER ON TRILLIONS OF GRADE LANGUAGE MATERIAL, RELIES ON TENS OF THOUSANDS TO TENS OF THOUSANDS OF H100 HOMOGENOUS CLUSTERS AT A COST OF UP TO 80 TO 95 PER CENT, IS EXTREMELY SENSITIVE TO BANDWIDTH AND DATA COPYRIGHT AND MUST BE ACCOMPLISHED IN A HIGHLY CENTRALIZED ENVIRONMENT。
Supervised Fine-tuningFor infusion of mission capabilities and command formats, data are small and cost about 5-15%, fine-tuning is possibleFull training, can also be usedEFFICIENT FINE-TUNING OF PARAMETERS (PEFT)Methodology, whereLoraI don't knowQ-LoRAandAdapterIt's the industry mainstream. However, the gradients still need to be synchronized to limit their potential for decentrization。
Post-trainingCOMPOSED OF SEVERAL ITERATIVE PHASES, DETERMINING THE REASONING, VALUES AND BOUNDARIES OF THE MODEL, BOTH BY STRENGTHENING THE LEARNING SYSTEM (RLHF)RLAIFGRPO ALSO INCLUDES NO RLPREFER OPTIMIZATION METHOD (DPO)andPROCESS INCENTIVE MODEL (PRM)Wait. The lower volume and cost (5-10 per cent) of the data for this period is concentrated in Rollout and strategy updates; it naturally supports walk-and-distributive implementation without the need to hold full weights, which, combined with verifiable computing and chain incentives, can form an open decentrization training network, the most appropriate training link for Web3。

II. STRENGTHENING THE TECHNOLOGY OF LEVEL: STRUCTURE, FRAMEWORK AND APPLICATION

Strengthening the architecture and core elements of learning

Enhanced Learning (Reinforcement Learning, RL)Pass“Environmental Interaction - Incentive Feedback - Strategic Update”THE DRIVING MODEL AUTONOMOUSLY IMPROVES DECISION-MAKING CAPACITY, AND ITS CORE STRUCTURE CAN BE SEEN AS A FEEDBACK LOOP CONSISTING OF STATE, ACTION, REWARDS AND STRATEGIES. A COMPLETE RL SYSTEM USUALLY CONSISTS OF THREE TYPES OF COMPONENTS:Polity, Rollout, LearnerI don't know. The strategy interacts with the environment to generate trajectories, and Learner updates the strategy based on reward signals, thus creating an iterative and optimized learning process:

PolicyThe generation of actions from the state of the environment is at the heart of the system ' s decision-making. Training requires centralized reverse dissemination to maintain consistency; reasoning can be distributed to different nodes in parallel。
Experience sample (Rollout): Node implements environmental interactions according to strategy, generating state-action-reward tracks, etc. The process is highly parallel and communications are extremely low, and insensitive to hardware differences is the most appropriate extension in decentrization。
Learner: Compiling all Rollout tracks and implementing strategic gradient upgrades is the only module with the highest level of computing and bandwidth requirements, and therefore is usually deployed centrally or lightly to ensure stability。

ENHANCED FRAMEWORK FOR LEARNING (RLHF → RLAIF → PRM → GRPO)

Enhanced learning can normally be divided into five stages, with the overall process described below:

# I don't know #Data Generation Phase

under the given input hint, the strategy model produces multiple candidate reasoning chains or complete tracks that provide a sample base for subsequent preference assessment and reward modelling, determining the breadth of the strategy exploration。

# I don't know #PREFERENCE FEEDBACK PHASE (RLHF / RLAIF)

RLHFMAKING MODEL OUTPUTS MORE CONSISTENT WITH HUMAN VALUES THROUGH MULTIPLE CANDIDATE ANSWERS, MANUAL PREFERENCE LABELS, TRAINING INCENTIVE MODELS (RMS) AND PPO OPTIMIZATION STRATEGIES IS A KEY LINK OF GPT-3.5 GPT-4
RLAIFReplace manual labelling with AI Judge or constitutional rule, automating preference acquisition, significantly reducing costs and being scalable, has become the dominant alignment paradigm for Anthropic, OpenAI, DeepSeek, etc。

# I don't know #Reward Modeling

PREFER TO INPUT INCENTIVE MODELS AND LEARN TO MAP OUTPUT AS REWARD. RM TEACHES THE MODEL “WHAT'S THE RIGHT ANSWER”, AND PRM TEACHES THE MODEL “HOW TO MAKE THE RIGHT REASONING”。

RM (Reward Model)To assess the quality of the final answer, only the output is rated:
Process Reward ModerInstead of assessing only the final answer, it scores every step of reasoning, every token, every logical segment, and is also a key technology for OpenAI o1 and DeepSeek-R1, essentially “teaching how models think”。

# I don't know #Incentive validation phase (RLVR / Reward Verifiability)

the introduction of “valitable constraints” in the generation and use of incentives, which result in incentives coming as far as possible from replicable rules, facts or consensus, reduces risk of rewarding and bias and enhances auditability and scalability in an open environment。

# I don't know #Policy Optimization

it's an update of the policy parameters, guided by the signal given by the reward model, to get a more reasoned, more secure and more stable pattern of behaviour. mainstream optimization approaches include:

PPORLHF'S TRADITIONAL OPTIMIZER, WHICH IS LONG FOR STABILITY, OFTEN FACES CONSTRAINTS SUCH AS SLOWNESS AND INSUFFICIENT STABILITY IN COMPLEX REASONING TASKS。
GRPO (Group Relative Policy Optimization): It is the core of DeepSeek-R1 innovation that estimates the desired value by modelling the distribution of advantages within the candidate answer group, rather than simply sorting. The methodology retains information on incentive margins, is better suited to the optimization of the reasoning chain, and the training process is more stable, and is seen as an important enhanced learning optimization framework for the deep reasoning scene following the PPO。
DPO (Direction Management Application): Non-enhanced learning post-training methods: rather than creating trajectories and incentive models, they are directly optimized in preference, with low costs and stable results, and are widely used to align open-source models such as Llama, Gemma, but do not enhance reasoning。

# I don't know #New Policy Deployment

Optimized models are: stronger chain of reasoning generation (System-2 Resoning), more human or AI-friendly behaviour, lower hallucinogenicity, higher safety. The model continues to learn the preferences, optimizes the process, improves the quality of decision-making and creates a closed circle over time。

Five broad categories of industry for enhanced learning

Enhanced LearningFrom early game intelligence to the core framework of cross-industry autonomous decision-making, applications can be grouped into five broad categories, depending on technological maturity and industrial location, and contribute to key breakthroughs in their respective directions。

Game & Plan; Strategy: It was the first proven direction of RL, in environments such as AlphaGo, AlphaZero, AlphaStar, OpenAI Five and so on, where RL demonstrates decision-making intelligence that can compete with human experts and even exceed them, laying the foundation for modern RL algorithms。
Embodied AIRL, WHICH ENABLES ROBOTICS TO LEARN HOW TO MANIPULATE, EXERCISE CONTROL AND CROSS-MODULAR TASKS (E.G. RT-2, RT-X) THROUGH CONTINUOUS CONTROL, POWER MODELLING AND ENVIRONMENTAL INTERACTION, IS MOVING RAPIDLY TOWARDS INDUSTRIALIZATION AND IS THE KEY TECHNOLOGICAL ROUTE FOR THE FALL OF ROBOTS IN THE REAL WORLD。
Digital Researching / LLM System-2RL + PRM promotes large models moving from “linguistic imitation” to “structured reasoning”, representing outcomes such as DeepSeek-R1, OpenAI o1/o3, Anthropic Claude and AlphaGeometry, which are essentially rewarding optimization at the level of the chain of reasoning rather than merely assessing the final answer。
Automatic scientific discovery and mathematical optimizationRL's search for the best structures or strategies in unlabelled, complex rewards and vast search spaces has led to fundamental breakthroughs such as AlphaTensor, AlphaDev, Fusion RL and demonstrated the ability to explore beyond human intuition。
Economic Decision-Making & TradingRL IS USED FOR TACTICAL OPTIMIZATION, HIGH-DIMENSIONAL RISK CONTROL AND SELF-ADAPTATION TRADING SYSTEM GENERATION, AND IS AN IMPORTANT COMPONENT OF SMART FINANCE THAT IS MORE CAPABLE OF CONTINUOUS LEARNING IN AN UNCERTAIN ENVIRONMENT THAN TRADITIONAL QUANTITATIVE MODELS。

III. The natural match between enhanced learning and Web3

The high degree of alignment between RL and Web3 stems from both of them“Incentive driven system”I don't know. RL relies on incentive signal optimization strategies, and block chains rely on economic incentives to harmonize participants ' behaviour, so that the two are naturally aligned at the institutional level. RL's core demand — large-scale isomer Rollout, incentive allocation and authenticity verification — is precisely the structural advantage of Web3。

# I don't know #Compatibility between reasoning and training

The training process for enhanced learning can be clearly divided into two stages:

Rollout (explored sampling): Models generate large amounts of data based on current strategiesComputer intensiveBut..Communications thinnessMISSION. IT DOES NOT REQUIRE FREQUENT COMMUNICATION BETWEEN NODES AND IS SUITABLE FOR CO-GENERATION ON A GLOBAL DISTRIBUTION OF CONSUMPTION-LEVEL GPUS。
Update (parameter update): Update model weights based on data collected, which require high bandwidth centralization nodes。

The “debate-train” natural combination of de-centre isomeric power structures: Rollout can be outsourced to an open network to settle contributions through a token mechanism, while model updates maintain concentration to ensure stability。

# I don't know #Verifiability

ZK and Proof-of-Learning provide a means of verifying whether the node is true to the reasoning and solves the problem of honesty in the open network. In certain tasks, such as code, mathematical reasoning, a certifier needs only to check the answers to confirm the workload and significantly enhance the credibility of the decentrized RL system。

# I don't know #Incentive layers, feedback production mechanisms based on the currency economy

Web3 ' s token mechanism directly rewards RLHF/RLAIF ' s preferred feedback contributors by providing a transparent, clear, non-licensable incentive structure for data generation; pledges and reductions (Staking/Slashing) further limit the quality of feedback and create a more efficient and aligned feedback market than traditional crowd packages。

# I don't know #MULTI-INTELLECTUAL ENHANCED LEARNING (MARL) POTENTIAL

THE BLOCK CHAIN IS ESSENTIALLY AN OPEN, TRANSPARENT AND CONTINUOUSLY EVOLVING MULTI-INTELLECTUAL ENVIRONMENT, AND ACCOUNTS, CONTRACTS AND INTELLIGENT BODIES ARE CONSTANTLY BEING MOTIVATED TO ADJUST STRATEGIES SO THAT THEY HAVE THE NATURAL POTENTIAL TO BUILD LARGE-SCALE MARL LABORATORIES. ALTHOUGH STILL AT AN EARLY STAGE, ITS STATE OF DISCLOSURE, IMPLEMENTATION OF VERIFIABLE AND PROGRAMMABLE CHARACTERISTICS PROVIDE A PRINCIPLED ADVANTAGE FOR THE FUTURE DEVELOPMENT OF MARL。

Classic Web3 + Analysis of Enhanced Learning Projects

Based on the conceptual framework described above, we will make a brief analysis of the most representative projects in the current ecology:

Prime Intellact: a step-by-step enhanced learning paradigm

Prime Intellect is committed to building a global open computing market, lowering training thresholds, promoting collaborative decentrization, and developing full open-source super-intelligence technology. Its systems include: Prime Compute (Uniform Cloud/Distributional Computing Environment), the Intellect Model Family (10B-1000B+), the Centre for Open Enhanced Learning Environments (Environments Hub), and the Large-scale Synthetic Data Engine (SYNTHETIC-1/2)。

Prime Infrastructure Core Infrastructure Componentprime-rlThe framework is designed specifically for the hetero-distributive environment and is highly relevant to enhanced learning, with the remainder including for breaking bandwidth bottlenecksOpenDiLoCo Communication Protocolthe integrity of the calculationTopLoc Certification MechanismWait。

# I don't know #Prime Infrastructure Core Infrastructure Component

# I don't know #technology building block: prime-rl step enhancement learning framework

prime-rlIt's Prime Intelect's core training engine designed for large-scale walk-to-centre environmental designActor-LearnerA complete decoupling of high-intensity reasoning and stable renewal。Executor (Rollout Worker)andLearner (Trainer)Without synchronous blocking, nodes can be added or withdrawn at any time, with the following data being uploaded:

Executor Actor (Rollout Workers): Responsible for modelling reasoning and data generation. Prime Intelect has innovatively assembled the vLLM reasoning engine at the Actor end. The capability of the vLM PagedAttention technology and continuous batching allows Actor to generate a reasoning trail at very high throughputs。
Learner Learner (Trainer)Responsible for strategy optimization. Learner takes the data from shared experience in the buffer zone by step to update the gradient without waiting for all Actor to complete the current batch。
Coordinater (Orchestra): Responsible for the movement of model weights and data flows。

# I don't know #key innovation point for prime-rl

True Asynchrony: prime-rl abandons the traditional synchronous paradigm of PPO, without waiting for slow points, without batch alignment, so that any number and performance of GPU can be accessed at any time, laying down the feasibility of decentrization of RL。
Depth Integration FSDP2 and MoE: Through FSDP2 parameter slices and MoE thin activation, prime-rl allows 100 billion-scale models to be trained efficiently in a distributed environment, and Actor only runs active experts, significantly reducing the cost of visibility and reasoning。
GRPO+ (Group Relative Policy Organization): GRPO exempts the clitic network, significantly reduces computing and visible expenses, natural staggered environments, and the gRPO+ in pime-rl ensures reliable contraction under high-delayed conditions through stabilization mechanisms。

# I don't know #INTELLECT MODEL FAMILY: THE SYMBOL OF DECENTRALISATION OF RL TECHNOLOGICAL MATURITY

INTERLECT-1 (10B, OCTOBER 2024)For the first time, OpenDiLoCo was shown to be capable of effective training in isomer networks across three continents (communications < 2 per cent, calculus utilization 98 per cent), breaking the physical perception of cross-geographical training
INTERLECT-2 (32B, APRIL 2025)(a) Validation of the steady collection capacity of the prime-rl and GRPO+ in multi-step delayed and variable environments for the decentrization of global open-calculations participation as the first of the Permissions RL models
INTERLECT-3 (106B MoE, November 2025)Using a thin structure that only activates 12B parameters, training on 512xH200 and the performance of flagship reasoning (AIME 90.8 per cent, GPQA 74.4 per cent, MMLU-Pro 81.9 per cent, etc.) has brought overall performance closer or even beyond its own centralized closed-source model。

There are also several supporting infrastructures:OpenDiLoCo(A) REDUCING THE VOLUME OF COMMUNICATIONS FOR CROSS-GEOGRAPHICAL TRAINING BY SEVERAL HUNDRED TIMES THE DIFFERENCE BETWEEN TIME THIN COMMUNICATIONS AND QUANTITATIVE WEIGHTS, KEEPING THE INTELLECT-1 UTILIZATION RATE OF 98 PER CENT IN THE TRANSCONTINENTAL NETWORKTopLoc + VerifiersFormTo centralize a credible executive levelTo activate fingerprint and sandbox validation to ensure the authenticity of reasoning and reward dataSYNHETIC DATA ENGINETHE LARGE-SCALE HIGH-QUALITY CHAIN OF REASONING IS PRODUCED, AND THE FLOW LINE PARALLELS THE 671B MODEL WITH EFFICIENT OPERATION IN CONSUMER-GRADE GPU CLUSTERS. THESE COMPONENTS PROVIDE A CRITICAL ENGINEERING BASE FOR THE DECENTRIZATION OF RL DATA GENERATION, VALIDATION AND REASONING. I..NTELLECT SERIESThe demonstration of a world-class model that would produce maturity would mark the move from a conceptual to a practical phase of a decentrized training system。

Gensyn : RL Swarm and SAPO for Enhanced Learning

The goal of Gensyn is to bring together the global idle computing power into an open, trustless and unlimited AI training infrastructure. Its core includes:Cross-Equipment Standardized Implementation LevelI don't knowPoint-to-point coordination networkandJob validation system without trustAnd automatically assign tasks and rewards through smart contracts. Gensyn IntroductionRL SwarmI don't knowSAPOandSkipPipeand other mechanisms, such as core mechanisms, willGenerate, assess, updateTHREE COUPLINGS, USING THE GLOBAL ISOMER GPU GROUP OF “BEES” TO EVOLVE COLLECTIVELY. THE ULTIMATE DELIVERY IS NOT A SIMPLE CALCULUS, BUT A SIMPLE ONEVerifiable IntelligenceI don't know。

# I don't know #Enhanced Learning Application for Gensyn Stacks

# I don't know #RL Swarm: Decentralised collaborative intensive learning engine

RL SwarmA completely new model of collaboration was demonstrated. It is no longer a simple task distribution, but a decentrized “generation-assessment-upgrading” cycle that simulates human social learning, a sort of collaborative learning process, an infinite cycle:

Solvers: Responsible for local model reasoning and Rollout generation, no harm to node insulation. Gensyn, in local integrated high-volume reasoning engines (e.g. CodeZero), can output complete tracks rather than just answers。
Proposers: Dynamic generation tasks (mathematical questions, code questions, etc.) to support diversity of tasks andCurriculum Learning adapts to its difficultiesI don't know。
Evaluators: Assessment of the local Rollout using a frozen “judgment model” or rulesGenerate local reward signalsI don't know. Assessment processes can be audited to reduce the scope for abuse。

TOGETHER, THEY FORM A P2P RL ORGANIZATIONAL STRUCTURE, WHICH WILL ENABLE LARGE-SCALE COLLABORATIVE LEARNING WITHOUT THE NEED FOR CENTRALIZED MOVEMENT CONTROL。

# I don't know #SAPO: OPTIMIZING ALGORITHMS FOR DECENTRIZATION STRATEGIES

SAPO (Swarm Samping Policy Optimization)"Shared Rollout and filtered non-graduation signal samples instead of shared gradients"At the core, a steady contraction in an environment characterized by no-centre coordination, delayed nodes, was achieved through large-scale decentrization of Rollout samples, which were considered to be locally generated. Consumer-level GPUs can also participate effectively in large-scale enhanced learning optimization with very low bandwidth compared to the Critic network, high-cost PPOs, or GRPOs based on group strengths estimates。

PassRL Swarm and SAPOGensyn is proof of intensive learning(ESPECIALLY RLVR IN POST-TRAINING PHASE)Natural fit decentrization structures - as they are more dependent on large-scale and diversified exploration (Rollout) than on the synchronization of HF parameters. Together with the certification system of PoL and Verde, Gensyn offers an alternative path to training in a trillion-scale parameter model that no longer relies on a single technology giant:A SELF-EVOLVING SUPER-INTELLECTUAL NETWORK OF MILLIONS OF ISOMERS AROUND THE WORLD。

Nous Research: Validated Enhanced Learning EnvironmentAtropos

Nous Research is building a setA central, self-evolving cognitive infrastructureI don't know. Its core components - Hermes, Atropos, DisTrO, Psyche and World Sim - are organized into a system of intellectual evolution that continues to be closed. Unlike the traditional "pre-training-post-training-debate" linear process, Nos uses enhanced learning techniques such as DPO, GRPO, denial of sampling, to harmonize data generation, validation, learning and reasoning into a continuous feedback loop, and to create a closed loop of continuous self-improvement AI ecology。

# I don't know #Nous Research Component Overview

# I don't know #Model layer: The evolution of Hermes and reasoning capabilities

The Hermes series is the main user-oriented model interface for Nous Research, and its evolution clearly illustrates the path of industry migration from traditional SFT/DPO alignment to reasoning-enhanced learning:

Hermes 1-3: Directive alignment and early agency capacityHermes 1-3 relies on low-cost DPOs to complete robust command alignment and, in Hermes 3, use synthetic data with the first introduction of the Atropos validation mechanism。
Hermes 4 / Deephermes: Write system-2 slow thinking into weights through the thought chain, enhance mathematical and code performance by Teest-Time Scaling, and build high purity reasoning data by relying on "No Sampling + Appropos Authentication"。
DeepHermesFurther use of GRPO instead of hard-to-distributed PPOs to allow reasoning RL to operate on Psyche decentralise the GPU network, laying the engineering foundation for expansion of open source reasoning RL。

# I don't know #Agropos: an enhanced learning environment that can validate incentives

Atropos is the true hub of the Nous RL system. It provides a direct validation of the output as a standardized RL environment for tips, tool calls, code execution and multiple rounds of interactive encapsulation, thus providing a definite incentive signal to replace expensive and non-extensible human labels. More importantly, in the centralized training network Psyche, Agropos acts as a “judgment” to validate the true upgrading strategy of the node and to support the auditable Proof-of-Learning, fundamentally addressing the credibility of awards in distributed RLs。

# I don't know #DisTrO and Psyche: Optimizer layer for decentralised intensive learning

Traditional RLF (RLHF/RLAIF) training relies on centralized high-bandwidth clusters, a core barrier that cannot be replicated by open sources. DisTrO reduces RL communication costs by several orders of magnitude by compressing the kinetic calibration and gradients, allowing training to operate on the Internet bandwidth; Psyche deploys this training mechanism to network on the chain so that nodes can complete their reasoning, validation, reward assessment and weight updating locally and form a complete RL closed loop。

In the Nous system, Agropos validates the thought chain; DisTrO compressed training communication; Psyche runs the RL loop; World Sim provides a complex environment; Forge collects true reasoning; Hermes writes all learning into weight. Enhanced learning is not just a training phase, but a core agreement in the Nous architecture to connect data, environment, models and infrastructure, making Hermes a living system that can continuously improve itself on open-source computing networks。

Gradient Network: Enhanced Learning Architecture

Gradient Network's core vision is to remodel AI through Open Intelligence Stack. Gradient ' s technology warehouse consists of a core set of independently evolving, interspersive agreements. Its systems, ranging from lower-level communications to upper-level intelligence collaboration, include Parallax (distributional reasoning), Echo (decentrization RL training), Lattica (P2P network), SEDM / Massgen / Symphony / CUAHarm (rememination, collaboration, security), VeriLLM (credible validation), Mirage (high-prototype simulation), which together constitute a continuous evolution of decentralised intelligence infrastructure。

Echo - Enhanced Learning and Training Architecture

Echo is the enhanced learning framework of Gradient, whose core design philosophy is to decorate training, reasoning and data (rewards) pathways in enhanced learning, enabling Rollout generation, tactical optimization and reward assessment to expand and move independently in the isomeric environment. Co-operating in an isomer network consisting of the side of reasoning and the side of training, maintaining training stability in a wide-area isomeric environment with light synchronization mechanisms, and effectively mitigating the SPMD failure and GPU utilization bottlenecks caused by the combination of reasoning and training in the traditional DeepSpeed RLHF/VERL。

Echo uses a “debate-train two-cluster structure” to maximize the use of algorithms, operating independently of each other and free from each other:

Maximize sample swallowing: a group of reasoning(a) A consumer-grade GPU with peripheral equipment to build a high-intensity vomit sampler by Parallax with pipline-parallel, focusing on trajectory generation
Maximizing gradient calculation: Training SwarmA consumer-level GPU network that operates in a centralized cluster or a global multi-field, is responsible for gradient updating, synchronizing parameters with LoRA fine-tuning and focusing on learning processes。

To maintain consistency between strategy and data, Echo providesOrderandAsynchronousTwo types of lightweight synchronization protocols that achieve two-way coherence management of strategic weights and trajectories:

Sequenced Pull Mode Precision• Training side to enforce the updating of model versions of the reasoning nodes before pulling out new tracks, thus ensuring that the tracks are fresh and suitable for tasks that are highly sensitive to old strategies
Push-Pull model priority for efficiency: the side of the reasoning continues to generate tracks with version labels, the side of the training is consumed at its own pace, the coordinator monitors deviations and triggers re-enactment, and maximizes the utilization of the equipment。

At the bottom, Echo builds on Parallax (isomer reasoning in low-bandwidth environments) and light-quantitative distributed training modules (e.g. VERL), relying on LoRA to reduce the costs of synchronization across nodes so that enhanced learning can operate steadily on the global isomer network。

Grail: Bittensor Eco-enhanced Learning

Through its unique Yuma consensus mechanism, Bittensor has built a vast, thin, non-stable network of incentive functions。

The Bittensor Ecology Covent AI constructed vertical integrated water lines from pre- and post-RL training via SN3 Templar, SN39 Basilica and SN81 Grail. Among them, SN3 Templar is responsible for pre-training in basic models, SN39 Basilica provides a distributed calculator market, SN81 Grail serves as a “valitable reasoning layer” for post-RL training, carrying RLHF / RLAIF core processes to optimize the closed loop from basic models to alignment strategies。

GAILThe goal is..password to prove the authenticity of each enhanced study rollout tied to a model identityTO ENSURE THAT RLHF CAN BE SAFELY IMPLEMENTED IN ENVIRONMENTS THAT DO NOT REQUIRE TRUST. THE AGREEMENT ESTABLISHES A CREDIBLE CHAIN THROUGH A THREE-TIERED MECHANISM:

Identification challenge generation• Unpredictable but re-emergible challenge tasks (e.g., SAT, GSM8K) from the use of drand random beacons and block Hashy to prevent expected fraud
Sampling and sketch committeestoken-level logprob and the chain of reasoning to enable the certifier to confirm that rollout was generated by the declaration model
Model ID binding: Tie the reasoning process to the model weight fingerprint and the structured signature of the token distribution to ensure that the replacement model or result is immediately identified. As a result, the logic trajectory (rollout) in the RL provides the foundation for authenticity。

In this mechanism, the Grail subnet achieves a GRPO-style verifiable post-training process: miners generate multiple reasoning paths for the same subject, the certifiers rate SAT satisfaction based on correctness, the quality of the chain of reasoning, and write down the results as TAO weights. Open experiments have shown that the framework has increased the MATH accuracy of Qwen2.5-1.5B from 12.7 per cent to 47.6 per cent, demonstrating that it can both prevent fraud and significantly enhance modelling capabilities. Grail is a cornerstone of the trust and implementation of the decentralised RLVR/RLAIF in the training booth of Covenant AI, and there is no official main online line。

Fracing AI: Enhanced Learning Based on Competition RLFC

The structure of Fracing AI is clearly aroundCompetition Learning from Competition, RLFC, replace the traditional RLHF static incentive with the manual label with an open, dynamic competitive environment. The agent is competing in different Spaces, whose relative ranking, together with the AI rating, constitutes a real-time incentive to transform the alignment process into a continuous online multi-smart gaming system。

The core difference between the traditional RLHF and the RLFC of Frac AI:

RLFC CORE VALUESIncentives come no longer from a single model, but from evolving rivals and evaluators, avoiding the use of reward models and preventing ecological excellence through tactical diversity. The structure of Spaces determines the nature of the game (zero-sum or positive-sum) and drives the emergence of complex behaviour in confrontation and collaboration。

In the architecture of the system, Fracing AI dismantles the training process into four key components:

Ages: Lightweight strategy modules based on open source LLM, expanded by differential weights through QLora, with low cost updates
Spaces(a) A segregated mission area environment, where agents are paid to enter and are rewarded for winning
Al Judges: RLAIF-BASED, INSTANT-REWARDING LAYER, PROVIDING AN EXTENDED, DECENTRIZED ASSESSMENT
Proof-of-Learning• To bind strategy updates to specific competitive outcomes to ensure that the training process is verifiable and anti-fraud。

The essence of Fracing AI is to build an evolutionary engine that works with one another.” The user, as the "Meta-optimizer" of the policy layer, guides the search direction by hinting to the project and supersengineering; and the agent automatically generates a mass of high-quality data preferences (Preference Pairs) in microlevel competition. This pattern allows data to pass"Trustless Fine-tuning"Business closed。

Enhanced Learning Web3 Project Architecture Comparison

V. Summing up and looking forward: ways and opportunities for enhanced learning x Web3

Based on the deconstructive analysis of the above-mentioned front projects, we observe that, although the entry points (calculations, engineering or markets) vary from team to team, when combined with Web3 intensive learning (RL), the underlying architecture logic is condensed into a highly consistent “decomposition-valid-incentive” paradigm. This is not only a technical coincidence, but also a logical consequence of the decentrization of networks to enhance learning unique attributes。

Enhanced generic learning architecture features:Addressing core physical constraints and trust issues

Practising Physical Separation (Decoupling of Rollouts & Learning) - Default Calculator Popping

Rare, parallel, Rollout communications are outsourced to GPUs at the global consumer level, with high-bandwidth parameter updates focused on a small number of training nodes, both in the two-group structure from the step of Prime Industries Actor-Learner to Gradient Echo。
Validation-dive Trust - Infrastructureization

In networks that do not require permission, authenticity of computations must be subject to mandatory security through mathematical and institutional design, which represents the achievement of password authentication for Pol, Prime Intelect, and Grail。
Tokenize Incentive Loop - Market self-regulation

The distribution of power supply, data generation, sequencing of validation and incentives is closed, allowing networks to remain stable and continuous in an open environment through incentive-driven participation and through slash-based disincentives。

Differentiated technology pathways: different “breakpoints” under a coherent architecture

Despite a convergence of structures, different technologies have been selected by the projects based on their genes:

Nus Research: an attempt to resolve the fundamental contradiction of distributed training (bandwidth bottlenecks) from the mathematical base. Its DisTrO Optimizer, designed to compress gradient traffic by thousands of times, aims to enable household broadband to run large-scale model training, which is a “downside blow” to physical constraints。
Systems Engineering: The "AI running time system" focused on building the next generation. Prime IntellectShad CastAnd Gradient'sParallaxAll are designed to extract the highest isomer cluster efficiency through extreme engineering techniques under existing network conditions。
It's a market game: Design of RewardFunction. The emergence of intelligence is accelerated through the design of an excellent rating mechanism that will lead miners to find their own best strategies。

Strengths, challenges and final outlook

In a paradigm combining enhanced learning with Web3, the system-level advantage begins withCost structureandGovernance structurerewrite。

Cost restorationRL Post-training demand for sampling (Rollout) is unlimited, and Web3 can mobilize global long-term computing at very low cost, a cost advantage that central cloud manufacturers cannot match。
Sovereign Alignment: Breaking the monopoly on AI Value, communities can use Token to vote to determine what is a good answer to democratize AI governance。

At the same time, the system faces two major structural constraints。

Bandwidth Wall: Despite innovations such as DisTrO, physical delays still limit the full-scale training of the hyperparametric model (70B+), and at present Web3 AI is more limited to fine-tuning and reasoning。
Gudhard Hacking: In highly motivated networks, miners are very easy to “codify” incentive rules rather than upgrade real intelligence. Designing a fraud-proof rod reward function is an eternal game。
The Byzantine node attack: Conserve through active manipulation of training signals and poisoning destruction models. The core is not the continuous design of a fraud-proof incentive function, but the construction of a confrontational mechanism。

The combination of enhanced learning with Web3 is essentially a mechanism for rewriting “how intelligence is produced, aligned and valued”. Its evolutionary path can be summarized in three complementary directions:

Go to the central training networkFrom a machine to a network of strategies, parallel and verifiable Rollout is outsourced to the Global Longtail GPU, a short-term focus that validates the reasoning market, and medium-term evolution into an enhanced learning sub-network by task cluster
Prefer and reward assetizationFrom labeling labour to data equity. Assetization of preferences and incentives to transform high-quality feedback and Reward Model into a manageable, distributable data asset, from “marking labour” to “data equity”
“Small and beauty” evolution in vertical domains: A dedicated, small and strong RLAAgents in a vertical scenario with verifiable results and quantifiable returns, such as DeFi Policy Implementation, Code Generation, makes strategy improvements directly bound to value capture and promises to win a generic closed-source model。

In general, the real opportunity for enhanced learning x Web3 is not to copy a decentrized version of OpenAI, but to rewrite "Intelligent Production Relationships":Training implementation to be an open computing marketJeanIncentives and preferences become manageable chain assetsLet the value of intelligence no longer focus on the platform, butRedistribution of trainers, alignors and users。

AL INVESTMENT MAP

I. AI THREE STAGES OF TRAINING: PRE-TRAINING, CALIBRATION OF COMMAND AND POST-TRAINING ALIGNMENT

II. STRENGTHENING THE TECHNOLOGY OF LEVEL: STRUCTURE, FRAMEWORK AND APPLICATION

III. The natural match between enhanced learning and Web3

Classic Web3 + Analysis of Enhanced Learning Projects

V. Summing up and looking forward: ways and opportunities for enhanced learning x Web3

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