Reading up on AI2

Tim Dettmers introduces SERA, a low‑cost method that fine‑tunes a 32B model on private code using synthetic data to match or exceed teacher‑model performance on that code.

Nathan Lambert argues open models will never catch up to closed frontier systems due to resource gaps, but are crucial for US AI research and policy as an engine for exploration.