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Models can autonomously bootstrap their own dense token-level supervision simply by extrapolating the trajectory of their own RL updates away from a trailing checkpoint.
Expanding an agent's harness with new tools and skills often degrades its performance on tasks it previously solved, exposing a critical failure mode of "harness-induced forgetting."
