Learning to Decide, Not to Reason: Parameter-Efficient Decision Operators via Low-Rank Activation Steering
Injecting skills into a frozen language model currently costs a million parameters and a reinforcement-learning pipeline. We introduce DecSteer, a System-1 decision operator trained by behavior cloning that lowers this cost by roughly two orders of magnitude. The default operator uses 330K parameters to match a 1.33M-parameter operator trained with reinforcement learning, exceeds or achieve comparable performance, while collapsing 3,685-token deliberation into a 6-token decision with no loss in accuracy. A rank-4 variant with 23K parameters, 1/58 of the strongest published skill operator, suffices for SearchQA and near-suffices for LiveMath, where higher rank still helps; the same recipe ...
| Authors | Ran Li, Lei Chen |
|---|---|
| Primary category | cs.LG |
| Published | 3 October 2026 |
| Last updated at source | 8 October 2026 |
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