Tandem training: RL method keeps strong models' reasoning auditable by weaker agents
manoelribeiro · x · 2026-09-06
David Bau amplifies an arXiv paper (West, Anderson, Kamar, Horvitz) arguing current frontier-model monitoring is like reading tea leaves. The paper formalizes intelligibility as handoff robustness: a strong model's solution is intelligible if randomly handing control to a weak model mid-solution doesn't cause failure. Tandem training intermittently samples rollout tokens from a frozen weak model during RL, so rollouts only succeed when the strong model's reasoning can be continued by the weak one. On GSM8K it reliably teaches models to drop jargon and adapt to weaker partners while keeping accuracy high—a route to AI systems auditable by weaker agents and humans.
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