Stable but Wrong: When Learning Stabilizes Away from the Truth
Stable training is often treated as evidence that learning is succeeding, but stability characterizes optimization behavior rather than correctness relative to an external objective. We study what happens when the signal being optimized remains persistently biased. We define Stable but Wrong (SBW) as a learning state in which the learning process remains stable under a task-appropriate operational criterion while the learned outcome remains systematically displaced from an independently defined objective. A minimal strongly convex model shows that a persistent bias in the update direction can shift the unique convergence point away from the true optimum. Controlled experiments in reinforc...
| Authors | Zhipeng Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 27 August 2026 |
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