🚨 Major Research Alert : Poisoning Attacks in Decentralised RL (GRPO) Are More Dangerous Than Expected @gensynai A new study uncovers a critical vulnerability in decentralised reinforcement learning systems specifically those built on Generalized Reinforcement Policy Optimization (GRPO). Because GRPO assigns a single scalar reward to an entire completion, even a handful of malicious nodes can secretly embed high-reward token patterns that trick the policy into updating in harmful directions. The result? 👉 Rapid model drift 👉 Widespread contamination across nodes 👉 System-level collapse in reasoning quality 🔥 Two Attack Vectors Identified 🔹 1. In-context attacks Attackers modify the actual reasoning traces, altering equations, logic steps, or chain-of-thought. This corrupts domain reasoning in math and code tasks. 🔹 2. Out-of-context attacks Attackers append irrelevant or nonsensical text that still receives high reward. This injects noise into the model’s style, structure, and distribution even when unrelated to the task. Both attack classes were shown to be highly effective in decentralized settings. 🛡️ Proposed Defenses (and When to Use Them) The paper introduces two powerful protection mechanisms: 1️⃣ Log-probability verification (for homogeneous models) Check if the model itself would plausibly generate the submitted tokens. If the sequence looks too unlikely → reject it. 2️⃣ LLM-as-a-Judge (for heterogeneous models) Use an external model to evaluate whether completions appear manipulated, corrupted, or adversarial. ✅ Results Both defenses: ✔ Block the majority of in-context and out-of-context poisoning ✔ Preserve the efficiency and scalability of decentralized GRPO ✔ Prevent malicious nodes from steering global policy updates 📌 Takeaway As decentralised RL becomes more widely adopted especially in community-driven or federated training environments robust defense mechanisms are no longer optional. Securing reward-based aggregation is essential to prevent silent model corruption and maintain long-term reliability.
- 0 replies
- 0 recasts
- 0 reactions
🚀 @gensynai: Powering Decentralized Machine Learning Gensyn unifies the world’s compute into a single, open network for AI. From laptops to data centers anyone can contribute, train, and verify machine learning workloads trustlessly. Built on Ethereum ⚙️ → Trustless verification → Fair coordination → Permissionless participation Explore the ecosystem 🌐 🧠 RL Swarm collective learning for RL agents 🎮 BlockAssist train AI inside Minecraft ⚖️ Judge decentralized runtime for ML verification Scaling AI beyond centralized limits. 👉 gensyn.ai
- 0 replies
- 0 recasts
- 0 reactions
🚀 @gensynai Public Testnet is Live The Dawn of Decentralized AI Infrastructure Launched in March 2025, the Gensyn Public Testnet marks a major leap toward a future where AI isn’t controlled by a handful of centralized players but powered, trained, and owned by everyone. Let’s break down what makes this testnet so important 👇 🔹 A Network Built for Decentralized AI The Gensyn Testnet introduces persistent identity to decentralized AI systems a foundation that lets contributors and nodes be recognized, rewarded, and verified transparently. It’s not just about compute. It’s about creating a full-stack network that can: ✅ Track participation ✅ Maintain attribution ✅ Coordinate remote execution ✅ Verify untrusted operations ✅ Log decentralized training runs ✅ Enable crowdfunding for AI training ✅ Handle payments fairly and automatically This is how open AI ecosystems begin where anyone can contribute to the growth of machine intelligence. ⚙️ Phase 0: The RL Swarm The current phase focuses on RL Swarm Gensyn’s collaborative reinforcement learning system that enables AI models to evolve by reasoning collectively over the internet. Each node that connects to the swarm becomes part of a distributed brain continuously learning, improving, and contributing. By linking your node to an on-chain identity, you can transparently track your participation and build reputation within the ecosystem. More details 🔗 docs.gensyn.ai/testnet/rl-swa… 🧩 Get Involved Whether you’re a developer, researcher, or just curious about decentralized AI there’s a place for you in the Gensyn ecosystem. 👥 Community Members Stay updated on progress, share feedback, and shape the protocol’s direction. Join the Discord. 🧠 Swarm Participants Run a node, contribute compute, and train local models while improving the intelligence of the network. 👨💻 Developers Train your own local model via the swarm and deploy it freely no corporate lock-ins. 🔬 ML Researchers Spin up your own swarm, explore new datasets, test novel training objectives, and pioneer decentralized machine learning frameworks. 🏗️ Under the Hood The Gensyn network runs on a custom Ethereum Rollup purpose-built for machine learning. It integrates: Off-chain execution for scalable compute On-chain verification for trust and transparency A communication framework enabling distributed AI coordination It’s not AI on blockchain it’s AI built into blockchain infrastructure. 🗺️ Roadmap to Mainnet The rollout is happening in carefully designed phases. Each phase brings new features and real-world testing opportunities. As more components go live, the network will evolve from post-training and swarm coordination → to full lifecycle ML operations: pre-training, training, fine-tuning, and inference. The final phase? Mainnet, where real economic value is transacted rewarding contributors, researchers, and node operators fairly for their work. 🌍 The Bigger Vision The Gensyn Testnet isn’t just another blockchain experiment. It’s the infrastructure for a world where AI is open, verifiable, and decentralized. Where your GPU, your data, and your intelligence contribute to a collective system that anyone can build upon. No gatekeepers. No black boxes. Just open, global machine intelligence built by the many, for the many.
- 0 replies
- 0 recasts
- 0 reactions