RLHF and DPO that align a model with your experts’ judgment.
SFT teaches a model what a good answer looks like. Preference tuning and reinforcement learning teach it which answer is better — the difference between competent and trusted.
Many enterprise tasks don’t have a single correct output. Two claim summaries can both be accurate while one is clearly more useful to an adjuster; two support replies can both be polite while only one resolves the ticket. These distinctions are hard to write as demonstrations but easy for an expert to judge side by side. Preference optimization turns those judgments into training signal.
We run the full family of methods. Direct Preference Optimization (DPO) and its variants train directly on chosen-versus-rejected pairs and are stable, cheap and effective. When the task has a checkable outcome — a correct tool call, a passing test, a matching code — reinforcement learning with verifiable rewards (GRPO, PPO) can push a model well beyond its demonstrations. Classic RLHF with a learned reward model remains the right tool when judgment is nuanced and data is plentiful.
Signs you need rlhf, dpo & preference tuning.
The right technique, chosen by the eval.
- DPO / IPO / KTO / ORPO. Offline preference optimization on pairwise or binary feedback. Our default first step after SFT.
- Reward modeling + PPO. Classic RLHF for nuanced quality with large preference datasets.
- GRPO with verifiers. RL against programmatic checks — unit tests, schema validators, database results, tool success.
- RLAIF & constitutional methods. Calibrated AI judges, audited against human raters, to scale feedback cheaply.
- Preference dataset with rater agreement statistics
- Reward model or verifier suite, documented
- Aligned checkpoints with win-rate analysis vs. SFT baseline
- Reward-hacking and regression audit
All assets transfer to you at handover. No licence fees.
Questions buyers ask us
What is the difference between RLHF and DPO?
RLHF trains a separate reward model on human preferences and then optimizes the language model against it with reinforcement learning. DPO skips the reward model and optimizes directly on the preference pairs. DPO is simpler and more stable; RLHF and online RL can go further when you have a good reward signal. Our comparison guide covers the trade-offs in depth.
How much preference data is needed?
DPO typically shows clear gains from 2,000–10,000 well-chosen pairs. Verifier-based RL needs prompts, not labels: a few thousand representative tasks with automated checks can be enough.
How do you stop reward hacking?
We hold out human-judged evaluations the reward never sees, inspect high-reward samples every round, cap KL divergence from the reference model and add adversarial checks to verifiers when the model finds loopholes.
Bring us the task. We'll bring the model.
A 45-minute technical briefing with a senior research engineer. You leave with a feasibility read, a data plan and a fixed quote — whether or not you hire us.
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