Yokkateg Prosesu Quiz Xalaatal ci Math

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Where does process supervision provide feedback compared with outcome supervision?
   • Only at the physical GPU temperature
   • At intermediate reasoning steps rather than only the final result
   • Only at the document filename
   • Only at an increased vocabulary size
2. Lan moo waral surveillance ci resultaa yi kese mën na réerloo nit ci jafe-jafe math?
   • Dafa yeex lool ngir xayma
   • Mënul jëfandikoo ay desimal
   • Dafay laaj grade nit ci jafe-jafe bu nekk
   • Benn model mën na am tontu bu jaar yoon ci jéego yu jaarul yoon yuy fomm
3. Lan la OpenAI génne ci wetu liggéeyu 'Nanu xool Jéego ak Jéego'?
   • PRM800K, ab done bu am ~800,000 etiketu niveau jéego
   • Architecture GPU bu bees
   • Test GSM8K
   • Motëru echec
4. In best-of-N math selection with a process reward model, what does the verifier do?
   • Guarantees that a correct candidate was generated
   • Changes every tokenizer token into a proof axiom
   • Scores generated candidate solutions under the chosen aggregation rule
   • Replaces the original problem with a translation
5. Ban gis-gis mooy wàññi soxla etiketu jéego yu seer yu nit ñi?
   • Defar xeetu neexal bi lëmm
   • Monte Carlo dafay xayma valeur jéego bu nekk ci saasi (lu melni, Math-Shepherd)
   • Jëfandikool tontu bu mujj bi kese
   • Tàggat ci yeneen làkk
6. What can low step-level verifier scores usefully indicate to someone reviewing a solution?
   • Steps that merit closer inspection
   • An infallible location of the first actual error
   • A mathematical proof that all other steps are correct
   • The exact temperature used to sample every token
7. For step scores 0.9 and 0.8, what is the solution score under a product aggregation rule?
   • 0.8
   • 0.85
   • 1.7
   • 0.72
8. Which data-selection strategy did Let’s Verify Step by Step study to improve labeling efficiency?
   • Choosing examples only by alphabetical filename order
   • Removing every incorrect-answer candidate from the labeling pool
   • Active learning that selected informative candidate solutions
   • Using an unlabeled test answer as the final human label