Into:grail
Proving AI got smarter, cryptographically.
As of · Jun 4, 10:37 UTC
The "finishing school" for AI. Where τemplar pre-trains the raw model, grail teaches it how to behave. It runs verifiable reinforcement learning, where every training step is cryptographically proven, meaning nobody can fake improvements or substitute models.
What is grail
grail is a post-training that takes pre-trained AI models and makes them better at specific tasks through reinforcement learning. The critical innovation is that every training step produces a cryptographic proof, so anyone can independently verify that the improvements are real and haven't been fabricated.
The simple version: Imagine a tutor who teaches a smart student how to actually pass exams. The student (AI model) already knows a lot from reading textbooks (pre-training on τemplar), but grail trains it to solve specific problems and prove each answer is genuine.
Centralized equivalent: Think OpenAI's RLHF (reinforcement learning from human feedback) pipeline that turns GPT base models into ChatGPT, but with cryptographic proof that the training actually happened.
How it works:
- generate multiple solution attempts ("rollouts") for assigned problems, tracking every token and probability. They solve math word problems (GSM8K) and logic puzzles (3-SAT), uploading their work with cryptographic commitments.
- derive deterministic problem sets from public randomness, verify each rollout against the GRAIL protocol (checking token commitments, model bindings, and solution correctness), then score based on unique, valid, and successful rollouts.
Why This Matters
Other research from the same neighborhood of the network.
