Post-training methods use training-side signals to decide which supervision to emphasize, but those signals need not identify behavior that is capability-bearing in the target environment. We probe this with DIAA (Dual-Implicit Adversarial Amplification), a per-sample σ = sigmoid(-βΔ) SFT/RL hybrid: after every literal Python code fence is deleted from the anchor data, the model still emits unexecuted Tool-Integrated Reasoning (TIR) form on 29% of MATH-500 prompts, three times vanilla GRPO, and suppressing the form recovers accuracy. A paired within-prompt intervention decomposes the removable drag along GRPO → SFT → DIAA as +2.2 → +8.5 → +10.6 pp, identifying SFT imitation and σ-amplification as comparable sources; a teacher RFT with 0% TIR likewise trains a student emitting TIR at 45.8%. We propose Intervention-Certified Amplification (ICA), which gates the per-sample weight by a certificate derived from paired rollouts, σ_eff(x) = σ(x)·c(x; θ_c). Frozen and online instantiations cut TIR to 13.8% and 11.2%, more than halve residual drag while improving accuracy, and carry the joint improvement to held-out GSM8K without retuning the certificate.