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Hyperparameter Guide
SFT / DPO Core Parameters
| Parameter |
What it controls |
Default |
Typical range |
| Epochs |
Passes through data |
2 |
1–5 |
| Learning rate multiplier |
Weight change aggressiveness |
1.0 |
0.1–2.0 |
| Batch size |
Examples per gradient step |
Model-dependent |
4–32 |
Dataset Size vs Epochs
| Dataset size |
Recommended epochs |
| < 100 examples |
3–5 |
| 100–500 examples |
2–3 |
| 500–2,000 examples |
1–2 |
| > 2,000 examples |
1 |
Learning Rate Guidelines
- Higher LR (1.5–2.0): Large/diverse datasets, task very different from pre-training
- Lower LR (0.1–0.5): Small datasets (<200), refining not overwriting base behavior
- For 1,000+ examples, LR 0.2–0.5 often beats default 1.0
DPO-Specific Parameters
beta (default 0.1): Alignment strength. Lower = more conservative.l2_multiplier (default 0.1): Regularization to prevent drift from base model.
HP Sweep Strategy
| Run |
Epochs |
LR |
Why |
| 1 |
2 |
1.0 |
Baseline |
| 2 |
2 |
0.5 |
Conservative |
| 3 |
2 |
1.5 |
Aggressive |
| 4 |
3 |
1.0 |
More training |
| 5 |
1 |
1.0 |
Minimal intervention |
Checkpoint Trick
When overfitting (val loss rises after epoch 2): deploy the epoch-2 checkpoint directly instead of retraining. Azure saves checkpoints at each epoch boundary.
checkpoints = client.fine_tuning.jobs.checkpoints.list(job_id)
for cp in checkpoints.data:
print(f"Step {cp.step_number}: val_loss={cp.metrics.valid_loss}")
Model-Specific Recommendations
| Model |
Recommended Start |
Notes |
| gpt-4.1-mini |
2ep, lr=0.5–1.0 |
Very capable base; small nudges work |
| gpt-4.1-nano |
2–3ep, lr=1.0–1.5 |
Smaller capacity, needs more epochs |
| gpt-oss-20b |
2ep, lr=0.2–0.5 |
Lower LR critical; deployment may need capacity=100 |
| o4-mini (RFT) |
Grader quality > HPs |
Focus on grader, not HP sweep |
OSS Model Parameters
All OSS models require trainingType: "globalStandard" in the API request.
| Model |
Recommended Start |
Best Found |
Notes |
| Ministral-3B |
5ep, lr=1.0 |
10ep, lr=0.5 |
Small model, slow convergence |
| gpt-oss-20b |
2ep, lr=0.3 |
2ep, lr=0.3 |
lr=1.0 overfits quickly |
| Llama-3.3-70B |
3ep, lr=0.3 |
5ep, lr=0.5 |
lr=2.0 causes catastrophic degradation |
| Qwen-3-32B |
3ep, lr=0.3 |
3ep, lr=0.3 |
Most fragile — more data can hurt |
Key patterns: OSS models need 2–5× more epochs than nano. Lower LR (0.3–0.5) is safer. More data doesn't always help.
RFT Hyperparameters
| Parameter |
Description |
Recommended Start |
reasoning_effort |
"low", "medium", "high" |
"medium" |
compute_multiplier |
Scales rollouts per step |
1.5 |
learning_rate_multiplier |
Scales LR |
1.0 |
n_epochs |
Data passes |
2–3 |
eval_interval |
Eval every N steps |
5 |
eval_samples |
Validation examples per eval |
10 |
max_episode_steps |
Max tool calls + reasoning steps |
5–10 |
