Trillion-Parameter Open-Source MoE Architecture & Scaled RL

### Sparse Mixture-of-Experts at Trillion-Parameter Scale The release of Xiaomi's MiMo-V2.6 marks a structural shift in open-source AI: trillion-parameter models with extreme routing sparsity that achieve frontier performance at accessible inference costs. 1. **Fine-Grained Routing and Shared Expert Anchors**: - Rather than routing between 8 or 16 large experts (like Mixtral 8x7B), modern trillion-parameter architectures deploy 128 to 256 fine-grained experts per layer. - Dedicated shared experts remain permanently active on every token, preserving baseline linguistic syntax while routed experts specialize in domain-specific tasks (e.g., formal logic, syntax parsing, cyber exploit verification). 2. **KV Cache Efficiency with Multi-Head Latent Attention (MLA)**: - Traditional Multi-Head Attention (MHA) creates an unsustainable memory bottleneck at 128k context lengths. - MLA projects key and value vectors into a low-rank latent subspace, reducing KV cache memory by over 70% and allowing 1.02T MoE models to run at long context on standard 8-way GPU servers. 3. **Verifiable Reinforcement Learning without Distillation**: - Rather than distilling soft probabilities from closed commercial models, teams run massive RL loops (750,000+ trajectories) inside sandboxed code execution environments (SWE-bench, CyberGym, formal proof verifiers). - Binary compiler and test outcomes provide ground-truth reward signals, eliminating hallucinated reasoning steps.