Traverse

Founded by Zachary Yu

Generate reinforcement learning environments and expert training data for frontier machine learning models

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★ Mixed Reviews

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Requires Expertise

You can use Traverse to build reinforcement learning environments and capture high-quality training data for complex machine learning models. The service focuses on subjective, non-deterministic tasks like legal analysis, strategic decision-making, healthcare workflows, and creative writing. By recording human experts working in realistic environments, it preserves the reasoning process behind difficult choices. Frontier research teams can then train models to develop sound judgment, navigate ambiguous situations, and complete long-horizon tasks that require deeper context rather than straightforward deterministic answers, expanding model capabilities beyond coding and math.

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What Traverse does

Record expert workflows in realistic software environmentsStructure reasoning paths for non-deterministic problemsGenerate multi-step evaluation benchmarks for agentsCurate post-training datasets for reinforcement learningModel human judgment in open-ended decision scenariosTrack context and constraints behind expert decisionsReinforcement learning environments tailored for non-deterministic tasksCapture expert reasoning and decision-making workflowsTraining data generation for subjective and long-horizon tasksDomain-specific context capture for law, healthcare, and salesReward modeling datasets for complex decision treesEvaluation frameworks for open-ended model outputs

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Frequently asked

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What does Traverse integrate with?−

Vision Data API, Rust (traverse-embedder crate), Swift / iOS (Preview), Kotlin / Android (Preview), Wasmtime / Wasmi (Runtimes), Salesforce (via Commercient SYNC), AWS (Infrastructure hosting), Python SDK, OpenAI API, Anthropic API, Custom Webhook Support, Docker for sandboxing, Unverified Cloud Storage Connectors, Salesforce (via Commercient), Rust (traverse-embedder), Docker, AWS Infrastructure, Wasmtime

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Traverse, reinforcement learning, RL environments, AI training data, LLM post-training, reward modeling, AI evaluation, machine learning, frontier AI, synthetic data, expert reasoning, subjective tasks, long-horizon work, judgment modeling, AI agents