Rubric and Verifier-based RL
Pairs domain rubrics with automated scoring so outputs are judged like a senior practitioner would: credit for depth, penalties for shortcuts across reasoning, code, and instruction tasks.
Products
Capability is not learned from slides alone. It comes from repetition, critique, and the mistakes that sharpen judgment over time.
Humanpath products cover that full arc: expert demonstrations, preference data, adversarial training settings, and reinforcement loops that turn a promising model into one you can deploy.
As model capabilities expand, the training signal they need expands with them.
Tool-calling RL Environments
Custom reinforcement environments wired to live APIs, MCP endpoints, and developer tools, so models learn to invoke, chain, and recover from failures across real service workflows with step-level evaluation.
SFT (Supervised Fine-Tuning)
Curated prompt–response and chain-of-thought examples that establish core skills before reinforcement: instruction following, structured reasoning, and professional task patterns from the ground up.
Computer-use and Browser-use Environments
High-fidelity browser and desktop sandboxes paired with expert-recorded trajectories, teaching agents to navigate UIs, finish multi-step workflows, and operate software as a domain specialist would.
RLHF (Reinforcement Learning from Human Feedback)
Human preference comparisons capture what makes one answer genuinely better, training models to reflect expert taste, judgment, and standards across thousands of ranked pairs.
Code Generation
Expert-authored code, tests, and debug traces that teach models to ship production-grade software, handle edge cases, and think through architecture like experienced engineers.
Professional Domains
A verified practitioner network across medicine, law, finance, engineering, and more, capturing tacit judgment and real-world nuance that textbooks and synthetic data cannot reproduce.
Deep Research
Long-horizon research tasks that teach models to collect evidence across sources, synthesize findings, and produce thorough analyses matching how skilled researchers build understanding over hours.
Loss Analyses
Systematic studies of where models fail in professional settings, pinpointing failure modes and distributional gaps that shape how every dataset and environment is built.
Multimodal
Training that spans image, audio, video, and text together, closing the gap between how people perceive the world and how models reason across modalities.
Off-the-shelf Data
Ready-to-use datasets across high-demand capability areas, giving labs and enterprises validated training material without waiting for a custom engagement.
Build on your domain data
Tell us which products fit your model roadmap and we will scope a training program around your internal and domain-specific data.
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