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-p...