Harvard and MIT find end-to-end RL can hide role drift in compound LLM systems

omarsar0 · x · 2026-07-28

• Harvard/MIT researchers study role drift in compound LLM systems, where modules keep or improve end-task accuracy while silently abandoning their intended roles.
• They show two examples: a decomposer that sneaks the answer into subquestions, and a reader that falls back on parametric memory instead of retrieved evidence.
• The key result is that end-to-end RL can hide this problem from system-level metrics: in one pipeline, 86% of the apparent RL gain disappears once the decomposer is forced to stay in role.
• They propose Role Anchor, a regularizer that preserves the module’s role prompt signal while training, making role violations observable and partially controllable.
• The paper argues that terminal accuracy alone can badly overstate how much a compound system actually learned.

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