Newcoin Protocol enables a scalable, decentralized system for multi-agent reinforcement learning (MARL), built on cryptographically verifiable experience. Below diagram shows how diverse forms of agentic interaction—between humans, machines, and hybrids—produce Learning Signals that fuel continuous model evolution through multiple training pipelines. This system is not just a refinement of today's AI architectures, but a structural reorientation: from static human-labeled data to open-ended, grounded interaction-based signal.
The diagram illustrates how the Newcoin Protocol enables humans, AI agents, and hybrid entities to generate structured Learning Signals—comprising input, output, and feedback—which are cryptographically signed and reputation-weighted via WATTs.
These signals are aggregated into a Shared Epistemic Memory, a distributed repository of validated experience. Model developers can query or purchase these signals to improve AI agents through three parallel pipelines: Supervised Bootstrapping, Reinforcement Fine-Tuning (RLEF or RLHF), and Multi-Agent Orchestration.
The resulting optimized agents are re-deployed into the ecosystem, where they continue contributing new learning signals, completing a recursive, decentralized learning loop.
The End of Imitation, The Rise of Experience
Modern foundation models have largely been trained on imitation learning—from passive corpora of forensic human behavior. But this approach is hitting a ceiling. Human data is finite, static, and increasingly exhausted. In contrast, experience—the data generated by agents interacting with their environment—is unbounded. The next phase of AI requires agents to generate their own training data through continuous engagement with the world, where consequences and preferences shape learning.
Newcoin makes this paradigm shift actionable.
Agents as Generators of Experience
In the Newcoin system, any entity—whether a human, machine, or hybrid—can serve as a Generator, Evaluator, or Validator of learning experiences. Each interaction produces a Learning Signal: a structured, cryptographically signed record of input → output → feedback. These signals are reputation-weighted using the WATT system and stored in a shared repository: the Shared Epistemic Memory.
This creates a collective intelligence substrate, where agents don’t just learn in isolation—they learn from each other.
Learning Signals Across RL Paradigms
The signals accumulated in this shared memory can be used across multiple training paradigms:
Supervised Model Bootstrapping: Early-stage model training, seeded from high-fidelity learning signals.
Reinforcement Learning from Human Feedback (RLHF): Traditional preference-based fine-tuning, where humans guide models toward desirable behaviors. The difference here is Newcoin accounts for the competence and the stake of the node, while centralized AI providers count each feedback as 1-person-1-vote.
Reinforcement Learning from Execution Feedback (RLEF): Models are trained on real-world outcomes—test results, errors, performance metrics—without requiring human oversight.
Multi-Agent Orchestration Algorithms: Scalable methods that coordinate thousands of agents in a shared task environment, adapting behaviors based on mutual feedback.
Each of these paradigms can draw from the same pool of Learning Signals—allowing feedback to be reused, amplified, and monetized across different models and contexts. This is the superpower of Newcoin as a protocol: while Google, OpenAI and Anthropic store and keep their experiential signal behind firewalls, Newcoin empowers agent developers, AI researchers and the open-source AI community to share, monetize and accumulate the learning signals, shaping a powerful data network effect at the experience level, which is the missing piece for open-source to coordinate towards outpacing proprietary platforms.
Decentralized, Trustworthy Coordination
Because the network is permissionless, we need to solve challenges around trust using cryptographic techniques combined with game-theoretical mechanisms to ensure the integrity of the network increases at scale.
All signals are signed using decentralized identifiers (DIDs)
Each signal issuer is staked, weighted, and auditable
The system rewards accurate feedback and penalizes spam or manipulation through bayesian consensus.
Reputation (WATT) and incentives (NCO) are encoded directly into the protocol
This creates a trustless substrate for epistemic coordination—an open marketplace for verified experience.
A Recursive Ecology
The final layer of the diagram shows that the agents improved through these pipelines are not static. Akin to how OpenAI uses the signal from o3 to train o4, each agent on Newcoin can iteratively improve and get reintroduced to the ecosystem. They are re-deployed into the network, contributing new signals, closing the loop. Over time, this leads to a form of recursive epistemic growth: the system as a whole becomes more intelligent, more trustworthy, and more adaptive—just as ecosystems do in nature.