# 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?

- 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.

## Fine-Tuning Guide

## Fine-Tuning Guide