# RL-Trained Conductor Orchestrator > A small RL-trained conductor sits in front of a pool of frontier LLM workers — learning which worker to call for which subtask from task-outcome rewards rather than hand-written routing rules. - **Category**: Agentic AI - **Subcategory**: Multi-Agent - **Canonical URL**: https://designpattern.fyi/patterns/rl_conductor_orchestrator/ --- ## Description **Intent**: Train a small meta-model with reinforcement learning to dynamically dispatch sub-tasks across a pool of frontier LLM workers, learning the communication topology end-to-end rather than hard-coding routing. **Context**: A production multi-agent stack dispatches subtasks across a heterogeneous pool of frontier LLMs from different vendors — one strong at long-context summarization, one at code synthesis, one at image understanding. The routing logic is hand-written if-then rules that can't keep up as the vendor pool changes and tasks span many domains. **Solution**: A small conductor model sits in front of a pool of worker LLMs and tools. On each step the conductor emits a natural-language subtask instruction and a worker selection; the worker runs, its output is returned, and the conductor decides the next move. The conductor is trained with RL against final task rewards — it learns which workers handle which subtask shapes, how to phrase the handoff, when to stop, and when to recursively dispatch a subtask back to itself. Workers remain frozen frontier models; only the conductor is trained. ## Use Cases - A heterogeneous frontier-model worker pool is in production and routing quality materially affects outcomes. - Task-outcome rewards are observable at scale. - An RL training pipeline (or a partner who provides one) is available. ## Trade-offs ### Advantages - Routing improves from experience instead of hand-editing rules on each model release. - Cheap meta-model on the hot path — frontier models are only called as workers when selected. - Recursive self-dispatch handles decomposable subtasks without a separate planner agent. - Worker pool churn is absorbed by retraining the conductor, not rewriting routing logic. ### Considerations & Drawbacks - Requires a reward signal and an RL training pipeline — most teams don't have this in-house. - Conductor policy can be opaque; a learned routing tree is harder to audit than a written one. - Recursive self-dispatch needs strict depth and budget caps or it can fan out aggressively. - Worker drift (vendor updates a model) silently changes the policy's effective action semantics. --- **Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/rl-conductor-orchestrator/)