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The missing mile between a Foundational model
and one that's agentic in nature
The limitations of current LLMs
THE 440HZ SOLUTION
Synthetic Training: Dynamic Gymnasium environments generate task-specific training data on-demand instead of relying on internet scraping.
Privacy-Preserving Edge Compute: Train on proprietary data through secure Docker-in-Docker containers on decentralized nodes. Encrypted LoRA deltas are aggregated—raw data never leaves.
Federated Aggregation: Adapters are encrypted, verified via ZK proofs, and merged using Flower consensus before writing to 0G Storage.
Pre-deployment Testing: Stress-test every agent in sandboxed Arenas with live reward functions before production deployment.
Why this matters now: The next generation of AI doesn't live on static datasets—it lives in dynamic, adaptive environments. Enterprises need to compete without exposing their intelligence. Autonomous agents must be tested under pressure before deployment. 440hz makes all of this possible on decentralized infrastructure. The future of AI tuning is not centralized scrapers on cloud providers—it's distributed, private, and on-chain.
/gym
A 440hz Gym is a Gymnasium environment packaged for on-chain distribution. Write your reward function, publish it to 0G Storage, and earn royalties every time a tuner trains on your environment.
Write reward functions in Python using the full Gymnasium API. Add RLAIF oversight nodes for nuanced scoring.
Wire observation spaces, action handlers, and data sources using the visual node graph or Monaco editor.
Upload to 0G Storage. List on the marketplace. Collect 80% of royalties from every training job that uses your gym.
5% platform fee · 80% builder royalties · 10% treasury · 5% protocol
/tuning
Arenas orchestrate distributed LoRA training on secure edge nodes. Encrypted adapters are generated through secure data pipelines with ZK verification before federated aggregation.
GRPO, PPO, or DPO. Configure LoRA rank, learning rate, KL coefficient, and episode budget in the Arena wizard.
Executors run in isolated Docker-in-Docker containers on decentralized nodes. Encrypted LoRA weights are generated through secure data pipelines with zero exposure to raw data.
After local training, encrypted adapters are submitted to a Flower aggregator using weighted FedAvg. ZK proofs verify integrity before the merged adapter is written to 0G Storage.
/tech
Every layer of the stack is built on 0G's decentralized AI infrastructure.
Gym bundles, LoRA adapters, and training checkpoints are stored as content-addressed blobs. Root hashes are the universal identifier.
A customized fork of 0G Compute optimized for RL and heavy AI fine-tuning. Training jobs run inside TEE-isolated executor containers on decentralized edge nodes. Raw proprietary data never leaves the enclave.
Computational proofs are verified using ZK technology to guarantee training integrity and provider accountability. The chain verifies proofs before settlement and payment.
GymMarketplace handles listings, purchases, and royalty distribution. TrainingEscrow holds compute payment in escrow per job.
Every user, gym, model, and weight set gets a human-readable subname under 440hz.eth. Issued on Base Sepolia via the Durin L2 registry — e.g. sigma-coder.440hz.eth, sql-v3-gym.440hz.eth.
/roadmap
440hz is evolving rapidly. Here's what we're building to expand the platform.
Build complex multi-agent environments with competitive or cooperative dynamics. Agents train simultaneously with federated weight aggregation.
Integrate MuJoCo and PyBullet for physics-based training. Robotic manipulation, locomotion, and spatial reasoning at scale.
Pre-built gym templates for common domains: code generation, SQL optimization, API orchestration, customer support automation.
Chain-of-thought and multi-step reasoning inside environments. Models learn complex workflows, not isolated tasks.
Start building today. The 440hz console is open. Create your first gym, submit a training job, or spin up a provider node to earn rewards.