Post-training Kimi K3 with Harvey for long-horizon legal work

Harvey and Fireworks announced Tenet, a model post-trained from Kimi K3 using asynchronous reinforcement learning to improve long-horizon legal-agent work. On the Legal Agent Benchmark, which requires completed legal deliverables to satisfy every grading criterion, Tenet achieved a 19.7% all-pass rate versus 10.8% for the base model and set a leading result on LAB Contracts, while also improving on unseen corporate-law and contract-redlining agent benchmarks. The model retained comparable performance on broader legal knowledge and reasoning evaluations and operated at nearly the same per-task cost as Kimi K3 despite completing almost twice as many LAB tasks. Fireworks attributes the training effort’s scalability and reliability to shared training-and-inference numerics, which reduce instability during long reinforcement-learning runs, and deterministic batch-invariant serving, which makes evaluations reproducible; the partners plan to publish a more detailed account of the reward design and training methodology.

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