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Outcome-based preference optimization collapses multi-path agent reasoning into brittle single-track policies, but balancing odds across divergence trees preserves viable alternative trajectories and significantly improves error recovery.
Spurious reasoning persists even after an end-of-think token is injected, complicating the transition to answering in large reasoning models.
