When AI companies like OpenAI or Anthropic build models like ChatGPT or Claude, they don’t just train the model once and ship it. They spend months doing something called “post-training”, teaching the model to actually be helpful, follow instructions, and give good answers instead of just predicting text.

This post-training is expensive, secretive, and controlled by big tech companies. Grail is trying to change that by making post-training decentralized, verifiable, and open to anyone.

Running as Subnet 81 on Bittensor, Grail uses reinforcement learning to turn basic AI models into smarter systems that can solve math problems, write code, and handle complex tasks. And unlike centralized companies, everything Grail does is public and verifiable.

What Grail Actually Does

Grail focuses on one specific part of AI development: making trained models better through reinforcement learning.

When AI models first finish their initial training, they’re like students who’ve read all the textbooks but haven’t actually practiced using that knowledge. They can generate text that looks plausible, but they’re not very good at specific tasks like solving math problems or writing working code.

Reinforcement learning is how you teach them to get better. You give the model problems to solve, check whether the answers are correct, and reward good performance while penalizing mistakes. Over thousands of examples, the model learns to actually solve the types of problems you care about.

Big tech companies do this behind closed doors with proprietary datasets and secret methods. Grail does it openly on Bittensor’s decentralized network, where anyone can participate and everything is verifiable.

The process works through what they call the GRAIL protocol (Guaranteed Rollout Authenticity via Inference Ledger). This is a fancy way of saying they use cryptographic proofs to verify that the AI training actually happened correctly and nobody’s cheating.

How The System Actually Works

Grail has three types of participants working together. Miners run AI models and generate what are called “rollouts”, basically, the model attempting to solve problems. For example, the model might try solving math problems from a dataset called GSM8K. The miner records these attempts, including whether the model got the answer right or wrong.

Validators check that these rollouts are legitimate. They use cryptographic verification to make sure miners actually ran the model and didn’t just fake the results or copy from someone else. This verification is hardware-agnostic, meaning it works regardless of what kind of computer the miner is using.

Trainers take the verified rollouts and use them to make the model better. They apply something called Group Relative Policy Optimization, which is a technique for training AI through reinforcement learning. The model learns from its successes and failures to get better at the tasks.

All of this happens openly. Training logs are public, and code is open source, so anyone can verify what’s happening and check the results.

What Makes This Different From Big Tech AI

When OpenAI trains ChatGPT or Google trains Gemini, the whole process is a black box. You have no idea what data they used, how they trained the model, or whether their methods actually work as claimed. You just have to trust them.

Grail works completely differently. Everything is permissionless, meaning anyone with a computer can participate. No need to work for a specific company or get approval. All the training is verifiable through cryptographic proofs. You don’t have to trust anyone because you can verify the work yourself. The code and methods are completely open source. Anyone can see how it works and suggest improvements.

The training also uses distributed computing instead of centralized data centers. This means people around the world contribute their computers rather than one company owning massive server farms. It’s potentially cheaper and more resilient.

Big tech companies use reinforcement learning, too, but their version requires huge teams, proprietary datasets, and expensive infrastructure. Grail proves you can do the same thing in a decentralized way with economic incentives coordinating everyone.

Why Anyone Would Invest in SN81

Grail has its own SN81 token used within the subnet. Buying this token is essentially betting that Grail will succeed in becoming an important part of how AI models get trained.

The token is also needed to participate in the network. Miners and validators need to stake SN81 or TAO to register and earn rewards. As more people want to participate because Grail is successful, demand for the token potentially increases.

There’s also the broader bet on decentralized AI. If people believe AI training should be open and verifiable rather than controlled by a few companies, Grail is building that alternative. The current market cap is around $8-10 million, which is tiny compared to what an important piece of AI infrastructure could be worth.

The Bittensor ecosystem itself is growing, with institutional investors like Stillcore Capital and DCG investing millions. When the overall ecosystem grows, successful subnets benefit.

How This Compares to Other Bittensor Subnets

Grail isn’t the only subnet focused on AI training. There are several others with overlapping goals.

Gradients (Subnet 56) does general fine-tuning of AI models for specific tasks. And Distributed Training (Subnet 38) coordinates training a single model across many GPUs.

Grail’s specific focus is reinforcement learning with cryptographic verification. While others handle general training, Grail specializes in the post-training phase that makes models actually useful for specific tasks like math and coding.

The GRAIL protocol for verifiable rollouts is unique. Other subnets might check that models work correctly, but they don’t have the same level of cryptographic proof that prevents cheating in an open network where anyone can participate.

Grail is also part of a larger ecosystem called Covenant AI that includes subnets for pre-training models, providing computing infrastructure, and alignment research. This creates a complete pipeline from raw training to deployed AI systems, which standalone competitors don’t match.

How Regular People Can Actually Use This

If you want to be a miner, you need a decent GPU; something like an NVIDIA RTX 3060 is enough to start. You download the open-source code from Grail’s repository, and stake some TAO or SN81 tokens to register. Then you run the mining software that generates rollouts using models like Qwen2.5-1.5B and submit them with cryptographic proofs. You earn TAO rewards based on the quality of your contributions.

If you want to be a validator, the setup is similar, but you’re checking other people’s work instead of generating rollouts yourself. You verify the cryptographic proofs and score submissions. Validators also earn rewards.

If you just want passive exposure, you can buy SN81 tokens on decentralized exchanges and stake them to earn a share of rewards without running any infrastructure yourself. This is simpler, but you’re trusting others to do the technical work.

For people who just want to use the improved AI models, once they’re trained, they get published to public storage and Hugging Face. You can download them and run them locally or integrate them into your own applications. No token needed for this part, as the models are open source.

The Discord community has guides for all of this, and you don’t need advanced coding skills for basic participation. But some technical comfort helps, especially for mining and validation.

The Team Behind Grail

Grail is developed by Templar AI, the team behind multiple Bittensor subnets in the Covenant AI ecosystem.

Samuel Dare leads the overall vision for Covenant AI and conceived the incentive-driven training model behind Grail. He’s a blockchain veteran who’s launched multiple Bittensor subnets and drives the strategy for the ecosystem.

The technical team includes AI researchers like Joel Lidin, who works on Grail’s verification protocol, and Amir Sarfi, who focuses on the reinforcement learning implementation. Eugene Belilovsky, a research professor at Mila and Concordia University, advises on the machine learning aspects.

The team published their technical approach in a 2025 research paper on arXiv and presented at conferences like NeurIPS. They completed the first permissionless large language model pre-training on Bittensor and received validation from Anthropic co-founder Jack Clark.

All code is open source on GitHub under the one-covenant organization, and they’re gradually decentralizing control through Bittensor’s mechanisms.

The Current State and What’s Next

Current work focuses on training models for math reasoning using datasets like GSM8K, coding tasks, and preparing for multi-turn conversations. The goal is to achieve state-of-the-art performance through decentralized training that matches or beats what centralized companies do.

They’re expanding to harder datasets and mixed training approaches. Real-time monitoring through tools like Grafana dashboards lets participants track training progress. All checkpoints and training logs are public, so anyone can verify results.

The broader vision is to be the reinforcement learning layer for the entire Bittensor ecosystem. Other subnets handle pre-training foundation models, providing compute infrastructure, or doing alignment research. Grail sits in the middle, turning those foundation models into actually useful AI systems.

As AI markets grow, projected to hit $1.5 trillion by 2030, the infrastructure for training these models becomes increasingly valuable. Grail is betting that open, verifiable training wins over closed, secretive methods.

Whether that bet pays off depends on execution and adoption. Can decentralized RL match centralized quality? Will developers choose open models trained this way over proprietary alternatives? Can the cryptographic verification scale as the network grows?

These are open questions. But for people who believe AI training should be transparent and accessible rather than controlled by a few companies, Grail is building that alternative right now.

Check out their GitHub at one-covenant/grail

Follow @grail_ai on X