Abojuto Ilana fun Idanwo Idiyemọ Iṣiro

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

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. Kini idi ti iṣakoso abajade-nikan le jẹ ṣina fun awọn iṣoro iṣiro?
   • O lọra pupọ lati ṣe iṣiro
   • Ko le mu awọn eleemewa mu
   • O nilo eniyan graders fun gbogbo isoro
   • Awoṣe le de ọdọ idahun ti o tọ nipasẹ awọn igbesẹ abawọn ti o ṣẹlẹ lati fagilee
3. Kini OpenAI tu silẹ lẹgbẹẹ iṣẹ 'Jẹ ki a Jẹrisi Igbesẹ Nipa Igbesẹ'?
   • PRM800K, ipilẹ data ti ~ 800,000 awọn aami ipele-igbesẹ
   • A titun GPU faaji
   • Eto idanwo GSM8K
   • Enjini chess kan
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. Ọna wo ni o dinku iwulo fun awọn akole igbesẹ eniyan gbowolori?
   • Pa awoṣe ere naa kuro patapata
   • Awọn iyipo Monte Carlo ti o ṣiro iye igbesẹ kọọkan laifọwọyi (fun apẹẹrẹ, Math-Shepherd)
   • Lilo nikan ik idahun
   • Ikẹkọ lori awọn ede diẹ sii
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