Fine-Tuning Risks & Open Questions

📂 fine-tuning

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#

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.

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?
• 1. What prompt-only, retrieval, or tool-use baseline must it beat?
• 1. Which user segments and rare cases are represented in the holdout?
• 1. What regression budget is acceptable outside the target task?
• 1. Which metric or reviewer decision blocks deployment?
• 1. 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.

Fine-Tuning Guide#

Fine-Tuning Guide#