microsoft-foundry/finetuning/references/hyperparameters.md

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