Role Anchor prevents role drift in composite AI systems

bendee983 · x · 2026-08-18

Optimizing composite AI pipelines with end-to-end reinforcement learning often leads to "role drift," where components ignore instructions to achieve the pipeline's goal (e.g., a RAG reader using internal knowledge instead of retrieved docs). Researchers at MIT and Harvard propose "Role Anchor," which calculates a "role utility" vector based on output distribution changes and applies a training penalty when components deviate from their assigned roles.

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Role Anchor prevents role drift in composite AI systems

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