Products & Research
Read about Gensyn's research initiatives, projects, and products.
Research
Each publication advances one or more of the protocol’s four core components: [1] execution, [2] verification, [3] communication, and [4] coordination.
Together, these projects form the scientific foundation for an open network that unites the world's compute into a single, trustless system for machine intelligence.
Current Projects
These projects collectively form the experimental backbone of the Gensyn protocol.
They are where new ideas are tested, validated, and refined before being integrated into the protocol so every architectural layer of Gensyn is grounded in reproducible, peer-reviewed science.
Verde A Verification System for Machine Learning over Untrusted Nodes A scalable verification protocol for decentralized machine learning. Verde introduces Reproducible Operators (RepOps), bitwise-deterministic ML primitives that ensure identical results across heterogeneous hardware, enabling trustless verification of model training. Research Paper Blog Post
NoLoCo
Training Large Models With No All-Reduce
A communication-efficient training method that eliminates global all-reduce synchronization. Using pairwise gossip averaging, NoLoCo achieves comparable convergence to standard distributed training at a fraction of the bandwidth cost. Research Paper Blog Post
CheckFree Fault-Tolerant Training Without Checkpoints Introduces a recovery mechanism that maintains training progress without traditional checkpoints, improving fault tolerance and throughput for distributed ML jobs. Research Paper Blog Post
SkipPipe Communication-Efficient Gradient Sharing Presents an optimization layer that reduces message hops and synchronization latency between nodes, forming part of Gensyn’s low-overhead communication backbone. Research Paper Blog Post
RL Swarm A Framework for Collaborative Reinforcement Learning Demonstrates how multiple models can train collectively across the internet, critiquing and improving one another’s outputs in real time. RL Swarm showcases decentralized coordination and collective learning in action. Research Paper Blog Post
Diverse Network Ensembles Embarrassingly Parallel LLMs From Diverse Experts Explores how heterogeneity in model size, training duration, and data domain leads to superior ensemble performance, laying groundwork for a global 'internet of models.' Blog Post Research Paper
BlockAssist A Playful Reinforcement Learning Environment An interactive Minecraft-based research demo where AI agents learn from player behavior. BlockAssist visualizes how distributed reinforcement learning frameworks like RL Swarm can operate in open, dynamic environments. Documentation Research Paper
Judge Cryptographically Verifiable AI Evaluation A protocol and runtime that ensures reinforcement learning tasks are executed fairly and verifiably across distributed environments. Judge provides a decentralized execution layer within RL Swarm, allocating, scheduling, and validating workloads while enforcing consistency and fairness across nodes. Blog Post
SAPO Efficient RL Post-Training Across Distributed Networks A fully decentralized and asynchronous reinforcement learning post-training algorithm where models share rollouts across a swarm. SAPO enables faster, more efficient collective learning with less compute per node, reducing communication overhead while maintaining performance. Blog Post Research Paper
Testnet The Network for Machine Intelligence A custom Ethereum rollup dedicated to machine learning, integrating off-chain execution, verification, and communication frameworks into a single permissionless network. The Gensyn Testnet coordinates global compute contributors and researchers, serving as the live backbone for decentralized AI. Read More GitHub
How Research Fits In
Each of Gensyn’s research initiatives contributes to the evolution of the protocol itself. The experiments, proofs, and frameworks developed here feed directly into the Core Components, which is how tasks are executed, verified, communicated, and coordinated across the network.
What begins as a research paper often becomes an open-source framework, then a working system deployed on the Testnet.
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