docs: explain first-iteration training skip - #394

Useful bookkeeping to pick up - the skip itself shipped in dc21dfe (#116, 2023-01-25), and #73 stayed open only because that PR used no closing keyword. I checked the mechanism end to end and the core of what you wrote is right: the skip branch does write the training script, does write a skip message to train.log, and does return init_model unchanged so the supplied models flow straight into exploration.

Two things to fix.

1. The new paragraph describes a predicate master has already superseded. Your branch is based on 6b01f29, where skip_training is (init_model is not None) and (iter_data is None or len(iter_data) == 0). Master took 7485ae7 ("fix: skip training with no expanded systems", #377) on 2026-09-02, which added a second trigger:

no_iter_data = iter_data is None or len(iter_data) == 0
if (init_model is not None) and (no_iter_data or training_systems_empty):

So the skip is no longer an iteration-zero-only phenomenon - it also fires in a later iteration whose configured inputs expand to zero training systems, and the log message now interpolates a reason instead of the fixed text. #377's own regression test test_exec_v2_fully_empty_training_systems builds exactly the case your sentence says cannot happen. "skips the iteration-zero training command" and "Training resumes after labeling produces iteration data" both need rewording after a rebase. The branch merges cleanly on text, so nothing forces this to surface - please rebase and re-read the paragraph against master's predicate.

While you are rewriting that paragraph, three more things belong in the same pass. They did not meet my bar to raise separately, but one rewrite fixes all of them:

• init_models_uri reaches the identical skip and in fact takes precedence over init_models_paths in submit.py; the dp-dist student_model_uri/student_model_path chain converges on the same init_models artifact too. Attributing the skip to init_models_paths alone documents one of three entry points, and doc_init_models_uri is left at the bare "The URI of initial models".
• "Training resumes after labeling produces iteration data" is true but incomplete in the way that matters operationally: init_model_policy defaults to "no", so iteration 1 trains from scratch and the supplied models are used for exactly one round of exploration unless the user also sets that policy.
• "those models were already trained on the initial dataset" reads as a second conjunct of a condition DPGEN2 evaluates. It is not - nothing checks provenance. The neighbouring "one model per committee member" clause is enforced (submit.py raises when the count differs from numb_models), which makes presenting the two identically more misleading than it looks. Phrasing it as a user obligation rather than a machine-tested condition would fix it.

2. do_finetune is documented in the wrong section, and the mistake it invites is silent. See the inline comment.

3. The new test assertion never executes in a regression. See the inline comment.

One thing I checked and am explicitly not raising, so it does not get re-opened: the four-sentence paragraph is not a style violation. The n_sample paragraph in the same file - blamed to 7794611 (#175), the commit that established this section's prose - is also four sentences with a worked example. Multi-sentence entries are house style when the argument needs them.