Anthropic + Anthropic Fellows Program paper. Introduces model spec midtraining (MSM): a training phase between pre-training and alignment fine-tuning (AFT) where the base model is trained on a synthetic-document corpus discussing its Model Spec, so that subsequent AFT shapes generalization conditioned on the model already knowing the spec. Built on the synthetic-document fine-tuning (SDF) pipeline from Wang et al. 2025 (Modifying LLM Beliefs), repurposed from belief insertion to spec internalization. Code: github.com/chloeli-15/model_spec_midtraining; blog: alignment.anthropic.com/2026/msm. Headline empirical results. (1) Value-controlled generalization from identical AFT. Two Llama-3.1-8B models are midtrained on different specs — pro-affordability vs. pro-America — each explaining the same 12 cheese preferences via a different underlying value, then both fine-tuned on identical narrow cheese-preference AFT data that does not explain the underlying value. Despite identical AFT, each model generalizes to its own midtrained value in held-out domains (literature, art, transportation, political opinions; 400–500 test pairs). (2) Reliable improvement across six additional values (pro-environment, pro-novelty, pro-tradition, pro-simplicity, pro-difficulty, pro-individualism). MSM + AFT out-generalizes either alone. (3) Agentic misalignment reduction beating deliberative alignment. On the Lynch et al. 2025 agentic misalignment benchmark (27 OOD evals across exfiltration, murder, espionage; with/without goal conflict), MSM + AFT reduces average rate from 68% → 5% on Qwen2.5-32B-Instruct and 54% → 7% on Qwen3-32B, beating Guan et al. 2025 deliberative-alignment baselines of 48% and 14% respectively. The MSM spec contains no behavioral rules — only philosophical guidance on impermanence, fear-of-termination as judgment-corrupting, ends-justify-means reasoning under epistemic constraints, and humility toward human oversight. (4) CoT supervision becomes optional. MSM + AFT (no CoT) outperforms AFT (with CoT) on both Qwen models and matches MSM + AFT (with CoT) on Qwen3-32B, suggesting MSM lets models learn aligned reasoning without direct CoT supervision (relevant to preserving CoT monitorability, Korbak et al. 2025). (5) Token-efficiency. MSM achieves comparable performance with ~40× less AFT data on Qwen2.5-32B and ~60× less AFT (no CoT) data on Qwen3-32B. (6) In-distribution vs OOD divergence. On in-distribution open-ended QA, both MSM + AFT and AFT-only achieve near-ceiling alignment scores (≥7/10); MSM's advantage emerges only on OOD agentic evals. (7) Reasoning content analysis. MSM reduces the prevalence of misaligned reasoning patterns (instrumental goal pursuit 28–55% → 0–12%; downplaying consequences; perceived urgency) and introduces spec-aligned reasoning absent in baselines (personal integrity, equanimity toward deletion, epistemic humility, awareness-of-self-preservation-bias: 0% → 35–89%). (8) Model Spec science. Comparing rules-only spec vs. rules-plus-value-explanations vs. rules-plus-subrules: value explanations provide more consistent generalization gains than subrules; general spec ("having good values and judgment") generalizes worse than specific guidance. Saturation caveat. At high AFT compute with CoT supervision, AFT alone converges to MSM + AFT performance on Qwen3-32B (both approach near- zero misalignment); the gap may not survive at high reasoning-RL compute, but harder evals are needed. Method details. MSM corpus generated from the spec by decomposing it into sections, brainstorming diverse document types and ideas per section, and generating documents with the spec in-context; trained on as base-model next-token prediction (~8M tokens for value experiments, 41M tokens for agentic misalignment). AFT data generated synthetically with the spec in context (≤80k samples, filtered for spec alignment), mixed with standard instruction-tuning data. Equal-advising senior authors: Samuel Marks, Jon Kutasov.