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## Sub-Agent Training & RLAIF (Demonstrations of Intelligence)
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While the Orchestrator delegates tasks live, the `nl_action_log` captured during **Frontier agent** (e.g., Gemini 3.1 Pro / Claude Opus) interactions via the Neural Link represents highly valuable "Demonstrations of Intelligence".
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While the Orchestrator delegates tasks live, the `nl_action_log` captured during **Frontier agent** (e.g., Gemini 3.1 Pro / Claude Opus) interactions via the Neural Link represents highly valuable "Demonstrations of Intelligence".
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Instead of treating these interactions as ephemeral, they are extracted into format-compliant `.jsonl` datasets for Reinforcement Learning from AI Feedback (RLAIF). The intelligence of the Frontier models is stockpiled to feed future SLM fine-tuning pipelines (SFT/DPO), directly raising the baseline capability of the local Sub-Agent tiers (like Gemma 4).
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## The Event Scheduler
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All work enters the system through the `Scheduler` — a priority queue that
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ensures critical events are processed before lower-priority ones:
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The **autonomous (unattended) runner** feeds its work through the `Scheduler` — a priority
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queue that ensures critical events are processed before lower-priority ones. (Interactive peer
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maintainers self-select work directly — see [Architecture Overview](../benefits/ArchitectureOverview.md)
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and [The Dream Pipeline](./DreamPipeline.md); the `Scheduler` is the unattended runner's intake,
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not a universal work source.)
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| Priority | Event Types | Example |
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## The Orchestration Pipeline
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The `Orchestrator` ties everything together. It reads the DreamService's
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prioritized roadmap and feeds it into the autonomous agent loop:
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The `Orchestrator` is the **unattended autonomous-runner** path — one advisory consumer of the
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handoff (peer maintainers self-select interactively). It reads the DreamService's prioritized
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roadmap and feeds it into the autonomous agent loop:
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