Update on @harvey’s model training effort. We post-trained a model we are calling Tenet: - Achieves SOTA on LAB - Generalizes 3rd party legal benchmarks - Uses sub-agents for domain specific capabilities Tenet uses Kimi K3 as base and was post-trained in collaboration with @FireworksAI_HQ: - Rank-64 LoRA over the full network - GSPO with importance-ratio masking - 134 B300 GPUs for 2 months Despite not being trained on 3rd party legal datasets we found improvements on: - @mercor’s Apex Agents - Corporate Law - @crosbylegal’s Redline Bench - LegalBench Tenet also learned how to use domain-specific subagents (separate post-trained models) for complex tasks: - M&A Diligence: training in an RLM harness for long-horizon tasks (with @baseten) - Review Table: specialist models for high-volume structured data extraction (with @appliedcompute) - Firm Knowledge: parametric memory and structured notes for more efficient enterprise search (with @engram) These results suggest we can significantly scale training and we plan to: - Scale both human and synthetic data significantly and scale training to 1K and then 10K GPUs - This scale will let us move to full parameter fine-tuning and larger models - We are now starting to post train models in our production harnesses - Post-train other open-source base models to provide customers with model choice If these problems sound interesting we are hiring for our post-training team