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Direct Preference Optimization
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Odds Ratio Preference Optimization
ORPO combines supervised fine-tuning with an odds-ratio preference penalty in one training phase and does not require a separate reference model.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
Its behavior depends on the preference data, objective settings, and implementation.
Kudzika Kwakadzika
Odds Ratio Preference Optimization starts with supervised fine-tuning on preferred responses and adds a penalty that contrasts the policy’s odds for chosen and disfavored responses. This monolithic objective avoids a separate reference model and a distinct preference-optimization phase. The paper describes the objective as a way to apply preference alignment during SFT; it does not mean that every data pipeline or implementation uses no auxiliary components. The odds transformation produces a different penalty shape from a raw log-probability difference. Outcomes depend on the preference dataset, coefficient, base model, and evaluation. ORPO is a training method, not an inference-time decoding rule or a guarantee that preferences are faithfully represented.
Kuitwa muNyika yeChokwadi
Kunyatsogadzirisa yakavhurika-sosi 7B chat modhi pane zvaunofarira vaviri pasina kurodha yechipiri referensi kopi, nepakati GPU memory.
Kutanga kuenzanisa mutengi-rutsigiro mubatsiri kuti asarudze zvine hunhu, pane-policy mhinduro mune imwe dzidziso inomhanya panzvimbo yeSFT-ipapo-DPO.
Vatsvagiri vanofananidza ORPO vachipesana neDPO pane imwecheteyo dataset kuratidza kuenzanirana kuenderana neyakaderera compute
Kuchinjisa modhi yehwaro kune yakasarudzika dura (semuenzaniso, kunyora zviri pamutemo) uko yakanaka uye yakaipa mienzaniso miviri iripo asi mubairo-modhiyo bhajeti haisi.
Ramangwana reOdds Ratio Preference Optimization
ORPO iri kuwana traction nekuti inocheka ndangariro uye komputa nekudonhedza referensi modhi, iyo inokwezva zvikwata kunyatso-tung pahardware shoma. Tarisira kuti ionekwe kazhinji muakavhurika-sosi yekubika uye senge yakasarudzika sarudzo mumaraibhurari seHugging Face TRL. Basa remangwana rinogona kurongedza uremu hwe lambda otomatiki, kusanganisa ORPO nezvimwe zvinangwa-zvisina referensi, uye kuiwedzera kune multimodal uye yakakura kwazvo modhi uko kubata makopi maviri mundangariro kunodhura.
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Take the Odds Ratio Preference Optimization quiz
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Mibvunzo inowanzo bvunzwa
What is Odds Ratio Preference Optimization?
ORPO combines supervised fine-tuning with an odds-ratio preference penalty in one training phase and does not require a separate reference model. Its behavior depends on the preference data, objective settings, and implementation.
Ndeipi yakakosha mukana weORPO pamusoro penzira dzakaita seDPO?
ORPO inopeta kudzidza kwekuda mukurasika kweSFT uye haidi kopi yakaomeswa yereferensi yemuenzaniso, kuchengetedza ndangariro uye komputa.
Chii chinofananidzwa neiyo 'odds ratio' muORPO?
ORPO inoshandisa reshiyo yemaodds (p/(1-p)) modhi yacho inopa mhinduro yakasarudzika maringe neyakasadiwa.
What form does an ORPO preference example contain?
ORPO uses chosen and rejected responses for the same prompt to form its preference comparison.
How does ORPO convert a response probability p into odds?
Odds are computed as p/(1−p); ORPO applies this transformation when forming its preference term.
Iko kurasikirwa kweORPO kunotsanangurwa zvakanyanya sekuti ndeipi musanganiswa?
ORPO inowedzera inorema log-sigmoid odds-ratio temu pamusoro pekutariswa kwemuchinjiko-entropy kurasikirwa.
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