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Harvey's release of Tenet marks a significant step in applying post-training techniques like asynchronous reinforcement learning to specialized legal tasks, demonstrating measurable gains on proprietary benchmarks without utilizing customer data. While currently available only as a research preview for enterprise clients, the methodology offers a blueprint for law firms aiming to deploy frontier intelligence on open-weight models while maintaining data sovereignty. The transferability of these improvements to third-party agent frameworks suggests that this approach may define the next phase of vertical-specific AI development in regulated industries.

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Harvey Introduces Harvey Tenet: A Kimi K3 Base Post-Trained with Fireworks for Long-Horizon Legal Agent Work

MarkTechPostAugust 23, 20267 min read

Harvey's first post-trained model nearly doubles LAB task completion, but only one benchmark number survives independent verification today The post Harvey Introduces Harvey Tenet: A Kimi K3 Base Post-Trained with Fireworks for Long-Horizon Legal Agent Work appeared first on MarkTechPost.

Harvey has released Harvey Tenet, its first post-trained model, as a research preview as of today. Tenet is a Kimi K3 base post-trained with Fireworks through asynchronous reinforcement learning on long-horizon legal work. The training corpus combined synthetic data, publicly available legal data, and human expert data. Harvey states no customer data was used. Against the base K3 model, Tenet completes almost twice as many held-out tasks on Harvey’s Legal Agent Benchmark (LAB) and 20% more on LAB: Contracts, raising all-pass rate by 9 and 2 percentage points respectively. Harvey reports state-of-the-art on LAB: Contracts and second place on LAB. The gains also transferred, untrained, to Mercor’s APEX Agents and Crosby’s Redline Bench. The stated goal is twofold: build frontier legal intelligence on open-weight models, and give law firms a path…

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