Per-token gating of forward/reverse KL losses has emerged as a key technique for on-policy distillation (OPD), but existing methods such as EOPD (Agarwal et al., 2024) and ToDi (Jung et al., 2025) each commit to a single gating signal and a single direction. It is unclear which signal works best for a given task, or whether gating helps consistently. We introduce a unified analysis framework: a four-coefficient parameterization 𝜎(a, h_t + b, u_x + c + d, gap_t) in which EOPD and ToDi appear as 1D restrictions, exposing multi-channel composition and an explicit bias term as additional structural degrees of freedom. On TweetEval emotion and hate, our framework improves over the matched-magnitude 1D restrictions in 33 of 36 comparable configurations (91.7%). A 26-configuration mean-match isolation experiment shows that dynamic OPD outperforms effective-KL-matched static baselines in 19 of 26 cases, with no configuration falling below the -1𝜎 noise band relative to its matched static counterpart. The optimal (a,b,c,d) is task-conditional: emotion favors entropy, hate favors prompt-level uncertainty.

For industrial content risk control, the real deployment constraint is not average accuracy but how much risk can be auto-handled under high precision and second-level latency. We present SIRF (Spec-Internalized Risk Foundation Model), which internalizes a platform’s complex policies, synthesized label-free via EntiGraph, MAGA rewriting, and account-level CoT, into the weights via continued pretraining, so rules are applied at high precision under an ultra-low-latency, classification-only deployment. A controlled same-source comparison (Qwen3-8B-SFT vs. SIRF-8B-SFT, identical policy injection and classification-only form, differing only in policy-grounded CPT) attributes the gain to internalization: SIRF-8B-SFT reaches 71.3 Black Recall@P95, +15 pp over the baseline, using only about 70M CPT tokens without harming general ability, and among included, logprob-available models under this interface it matches or exceeds far larger systems. SIRF is deployed as a tree-model adjudication layer (+20% more mis-penalized samples recovered) and transfers to a freezing scenario at low cost (~70% relative mis-penalization reduction).