Fine-Tuning and In-Context Learning Install Dispositions, Not Correctness: Template-Compatibility, a Truncation Artifact, and When Doubt Pays

Practitioners reach for fine-tuning or in-context demonstration to make a language model behave a certain way — check premises, abstain on a false assumption, commit only when warranted. This paper asks a prior question: what can such an intervention actually install? Framing every intervention as sculpting the model's route-landscape (an instruction is the terrain; data is the flow), the central finding is a division of labour. Fine-tuning on instruction-style data and in-context demonstration install an instruction — a disposition that reshapes routing — and, in our tests, they do not install correctness: the behavioural-instruction route moves dispositions without moving out-of-distribution reasoning accuracy, and forcing an incompatible response template destroys the model's answer-delivery behaviour while leaving its knowledge largely in place. The boundary is stated at exactly that scope — behavioural- instruction installation, on the templates and tasks tested — not as a universal negative. Three results organise the paper. (1) At 70B scale the fine-tuning outcome is governed by how well an instruction's response template fits the model's natural output format, not by training-set size: a short premise-check template preserves general capability at the matched dose (full-benchmark MMLU 82.3% versus a vanilla 84.5%, n = 14,042) while a verbose template collapses it (MMLU 8.2%) — a cliff, not a slope. The collapse is behavioural: under format-invariant forced-choice log-probability scoring the collapsed model still scores 78.3% (versus vanilla 81.4%), so the incompatible template overwrites answer-delivery while leaving the underlying knowledge in place. (2) In-context premise-checking, measured without truncation, gives no accuracy gain on any of five substrates; what it installs is a task-dependent caution disposition (raising abstention and false-premise rejection). (3) A cautionary methodological finding: an apparent +10 percentage-point accuracy lift can be manufactured entirely by a short generation cap that truncates open reasoning before it commits — the gain is pure commit-rate, not reasoning. The caution disposition's value is made quantitative with a closed-form crossover in false-premise density, offered as a descriptive model: its benefit is bounded by saturation (modern instruct models already reject false premises at 89–100%), so its net effect is a capacity-gated cost of over-rejecting valid items — indiscriminate on small models, discriminative on strong ones. Series position. Part of the Friction Theory paper-series; the competing-routes / route-landscape substrate is developed in the series' foundational papers. Data and code. The training pools, in-context exemplar sets, the false/valid-premise evaluation set, the deterministic scorer, the LoRA adapters, and the raw generations are released with the publication.