Preference Optimization

This skill assumes finetuning-method-selection already routed here because the data shape is preference pairs or unpaired thumbs-up/down feedback, not demonstrations (that's lora-qlora-recipes) or a verifiable reward signal (that's grpo-rlvr-training). What follows is method selection among the DPO family, the evidence for how much that selection actually matters, the production training pattern, and how to build the pairs in the first place.

Input: a routing decision (preference optimization) plus preference pairs or unpaired feedback, usually from an SFT checkpoint. Output format: a validated method choice plus a config — the kwarg values in references/method-configs.md, not free-form advice — that llm-finetuning-training-engineer consumes directly.

Method Selection

• DPO is the safe default. Use β=0.1 and a learning rate of 5e-7 to 1e-6 for 1–2 epochs. This LR is lower than the SFT LR that produced the checkpoint being aligned — porting an SFT- scale LR into a DPO run is the most common misconfiguration here, not an edge case.
• ORPO routes in when memory is the constraint, or when there's no separate SFT checkpoint to start from — it's reference-free and fuses the SFT and preference objectives into one loss, skipping the separate SFT pass and the reference-model memory cost DPO carries.
• KTO routes in when feedback is unpaired binary signal (thumbs-up/down) rather than matched preference pairs — don't force unpaired feedback into synthetic pairs to use DPO instead.
• SimPO fixes DPO's length bias but only pays off with disciplined sweeping — its published gains are a ceiling reported under a tuned sweep, not a baseline any single config will reproduce. Route here only when there's sweep budget; use DPO instead if there isn't.
• Classic RLHF (reward model + PPO) is retired outside frontier labs. Don't reach for it in a production pipeline — every method above is cheaper and better-supported for the same data shapes.

Worked Examples

• "We have an SFT checkpoint and clean paired preference data, no length-bias complaints yet." → default case → DPO at β=0.1.
• "Reviewers click thumbs-up/down per response; nothing is paired." → unpaired signal → KTO, not DPO — don't synthesize pairs to force DPO onto unpaired data.
• "GPU budget doesn't cover a separate SFT pass plus a DPO reference model." → memory-bound, no separate checkpoint → ORPO.
• "DPO output favors longer answers regardless of quality, and there's time to run a sweep." → length bias plus sweep budget → SimPO. Skip it if the sweep budget isn't actually there.

The Low-Leverage Truth

A 2026 240-H100-run study (arXiv 2603.19335) is the load-bearing evidence behind the table above: loss-function choice is worth roughly 1 percentage point of leverage, model scale is worth roughly 50. Zero of 20 DPO variants tested beat vanilla DPO. Rankings also invert with scale — a variant that wins in a small pilot can lose at deployment size.

Two practical consequences:

• Don't spend a routing decision agonizing over DPO-variant bake-offs. The table above is sufficient; deeper variant selection is low-leverage compared to data quality and scale.
• Validate at deployment scale before trusting a ranking. A method comparison run on a small pilot model doesn't transfer to the production size class — re-check the winner once scale changes.

This is also why the Method Selection table above is deliberately short: it encodes the ~1pp lever, not a ranking of DPO variants that the same study shows doesn't hold up across scale. Treat any variant-selection advice that isn't in that table — including advice that claims a specific variant "wins" — as unproven until it's been validated at the target deployment size.

Production Pattern: Iterative On-Policy DPO

A single offline DPO pass on a static preference dataset is a starting point, not the production

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