A “shadow steering” pattern lets a larger model intervene only when a smaller one starts to fail

dotey · x · 2026-07-21

The post proposes a “shadow steering” pattern: a cheaper model handles the normal workload while a smarter model stays in the background, maintaining state with near-zero visible output.
When the harness detects quality degradation, the larger model steps in briefly with a short guidance burst, nudging the smaller model back on track.
The author argues this is useful for reducing medium-model cost, distillation, testing, and long-horizon task quality collection, because the intervention is on-policy and hidden inside the trajectory.
They then speculate that the news implies DeepSeek’s next model is already trained and being shadow-tested, with users only noticing its improved intelligence indirectly.

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