An agent trajectory dataset records more than the final answer. It preserves the task, observations, tool calls, tool results, decisions, corrections, and outcome so a specialist can learn the behavior that produced success. The dataset is only useful when its provenance, privacy, and evaluation boundary are explicit.
Define the training unit
Choose whether one example is a full episode, one decision point, or a correction pair. Preserve the original task and enough state to understand each action. Avoid training on hidden evaluator feedback that the runtime will never receive.
Capture typed events
Normalize observations, assistant reasoning that is permitted for training, tool names, arguments, results, errors, and final outputs into a versioned schema. Keep raw source references so the transformation can be audited. Do not flatten every event into an undifferentiated chat transcript.
Filter for useful behavior
Remove duplicates, leaked tests, private values, malformed tool calls, and trajectories without an authoritative outcome. Separate successful examples from correction data and hard negatives. Preserve the reason an example was accepted or rejected.
Split by source and task family
Random row splits can leak nearly identical episodes across training and test sets. Group by repository, task template, session, or source artifact before splitting. Freeze the holdout before tuning the training recipe.
Train the narrowest useful target
A trajectory dataset can support supervised fine-tuning, preference pairs, distillation, routing, or an evaluator. Choose the objective from the observed failure. If the model over-edits, an objective that increases edit pressure is the wrong response.
Evaluate the behavior, not imitation
Measure whether the specialist selects the correct tool, produces valid arguments, reacts to tool results, stops appropriately, and completes the task. Also measure unsupported actions and breadth regressions.
PostTrainLLM includes trajectory conversion, correction-to-data, synthesis, quality filtering, deduplication, tool-calling evals, and factory-run evidence. Data and model training remain local and owner-controlled; public report cards should expose only safe, reviewed evidence.
CTA: Continue to the specialist-model workflow or inspect the factory documentation.