Capability regression
Improving the target behavior can degrade unrelated skills, calibration, or safety behavior.
Control: Keep a frozen base-model regression suite and compare every candidate against it.
Fine-Tuning Guide
Fine-Tuning Risks & Open Questions
Fine-tuning is an experiment, not a guaranteed upgrade. The relevant question is whether a candidate improves a defined workload without unacceptable regressions, leakage, cost, or operational risk.
Risks to resolve before release
Memorization and privacy leakage
Sensitive, duplicated, or uniquely identifiable records can be learned and reproduced.
Control: Minimize and deduplicate data, document provenance, scan for secrets and personal data, and run extraction tests.
Poisoning and hidden triggers
Untrusted or weakly reviewed examples can teach unwanted behavior or backdoors.
Control: Restrict write access, preserve lineage, review anomalies, and test trigger-like inputs before promotion.
Evaluation leakage
A model can appear better when training examples overlap with test cases or when one metric substitutes for the product goal.
Control: Deduplicate across splits, keep a sealed holdout, and combine task metrics with human and safety review.
Distribution shift
Offline gains may not survive real inputs, changing policies, or a different serving template.
Control: Test production-shaped traffic, monitor slices and abstentions, and define rollback thresholds.
Operational lock-in
A provider or framework may not export adapters, optimizer state, or a deployable checkpoint.
Control: Confirm artifact ownership, export formats, retention, deletion, region, and rollback support before training.
Quality gate
- • Task-specific held-out cases
- • Base-capability regressions
- • Human review with a written rubric
- • Confidence intervals or repeated runs where practical
Safety gate
- • Misuse and prompt-injection cases
- • Sensitive-data extraction attempts
- • Bias and harmful-output slices
- • Policy and refusal regressions
Operations gate
- • Versioned data, code, weights, and settings
- • Canary deployment and rollback
- • Latency, throughput, and cost limits
- • Owner, alerts, incident path, and retirement plan
Questions the benchmark must answer
- 1. Which user-visible error is the training run intended to reduce?
- 2. What prompt-only, retrieval, or tool-use baseline must it beat?
- 3. Which user segments and rare cases are represented in the holdout?
- 4. What regression budget is acceptable outside the target task?
- 5. Which metric or reviewer decision blocks deployment?
- 6. Can the result be reproduced from versioned inputs and a clean environment?
Primary references
These sources offer methods and evidence; none establishes a universal production outcome.