Fireworks AI released a technical report on how it speeds up reinforcement learning for its Kimi K3 model, a 2.8 trillion‑parameter system with a 1 million‑token context window. The company describes new kernels and communication tricks that give 1.3–2.6× faster forward and backward passes. Using the same setup, the Kimi‑based legal model Harvey Tenet scored 19.7% all‑pass on the LAB Contracts benchmark, beating the prior best Fable 5’s 11.5%.
Why it matters
The speed improvements let developers iterate on specialized AI policies faster, and the higher legal benchmark score shows the model can handle complex contract analysis better.
