Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Muunad-qaadista ugu Wanaagsan iyo Dib-u-dejinta Kediska

Fahmo xulashada-N-ka ugu wanaagsan, xaddidaadaha gooldhaliyeyaasha, kharashyada-waqtiga-fiirsashada, iyo malaha ixtimaalka ee ka dambeeya muunad musharraxiin badan.

Wadooyinka haga ee la xidhiidhaSida ugu Wanaagsan ee Samaynta iyo Dib-u-dejinta
Su'aal 1 ee 8

Waa maxay fikradda udub-dhexaadka ah ee ugu-wanaagsan ee muunada?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay fikradda udub-dhexaadka ah ee ugu-wanaagsan ee muunada?
    • Samee natiijada N oo dooro midka ugu fiican oo leh gool dhaliye
    • Tababar qaabka N waqtiyada oo celcelis miisaannada
    • Isticmaal kaliya wax soo saarka ugu horreeya ee moodelku soo saaro
    • U yaree erayada N tokens
  2. When is best-of-N most likely to help?
    • When every output is equally good
    • When the model sometimes samples a correct candidate and a reliable verifier can identify it
    • When the task has no way to assess candidates
    • When sampling is disabled
  3. Maxaa inta badan go'aamiya in-u-fican-N ay dhab ahaantii hagaajinayso natiijooyinka?
    • Midabka interface-ka isticmaalaha
    • Dhererka degdega ah kaliya
    • Tayada iyo adkaanta gooldhaliyaha ama xaqiijiyaha
    • Cabbirka erayada qaabka
  4. What can happen when N becomes large and the reward model is an imperfect proxy?
    • The model’s tokenizer is removed
    • The model forgets its training weights
    • All candidates become identical
    • Selection can favor outputs that exploit scorer errors and reduce true quality
  5. Istaraatiijiyadee fudud ee dib-u-dejinta ayaa sidoo kale loo yaqaannaa is-waafajinta?
    • Codeynta aqlabiyadda ee jawaabaha muunada ah
    • Doorashada jawaabta ugu dheer
    • Doorashada muunad random
    • Had iyo jeer qaadashada wax soo saarka ixtimaalka ugu hooseeya
  6. What mainly drives the generation cost of best-of-N?
    • Only the number of vocabulary entries
    • The total amount of text generated across the sampled candidates
    • The user interface color
    • The number of model training runs
  7. If each independent sample succeeds with probability p and a perfect verifier picks any success, what is the chance at least one of N samples succeeds?
    • p/N
    • N−p
    • 1−(1−p)^N
    • 1−p^N
  8. Which assumption is needed for the simple 1−(1−p)^N success formula?
    • Every response has a different length
    • The model is retrained after each sample
    • The reward model has no parameters
    • The candidate samples are independent and the verifier recognizes success perfectly