Templar builds the foundational knowledge. Grail sharpens the reasoning through RL post-training.
@erfan_mhi's PULSE paper showed how to make distributed RL practical by cutting weight sync bandwidth by 100x. This week
@FireworksAI_HQ confirmed the same approach works at 1T scale for
@cursor_ai Composer 2.
When Templar's next models are ready, Grail's infrastructure is proven at scale.
Paper: