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ORPO vs DPO — key difference.

hard

Answer

  • DPO: needs reference policy πref{\pi}_{\mathrm{ref}} (extra memory + inference cost during training).
  • ORPO: no reference policy — uses odds ratio between chosen/rejected.
  • Simpler infra (one model instead of two).
  • Comparable performance to DPO on many benchmarks.
  • Modern choice: ORPO when reference model is inconvenient (larger models, custom architectures) or when you want simpler infra.
Check yourself — multiple choice
  • Same thing
  • DPO: needs reference policy πref{\pi}_{\mathrm{ref}} (memory + inference cost); ORPO: no reference, uses odds-ratio → simpler; comparable performance; ORPO wins on infra simplicity
  • Random
  • Only DPO

ORPO: no reference policy; simpler than DPO; comparable performance.

#llm#alignment

Practise Reinforcement Learning

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