Lookhead Iyipada adanwo

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

1. Kini o ṣe iyatọ yiyan ti wokahead lati iyipada arosọ boṣewa?
   • O nilo awọn awoṣe ikẹkọ lọtọ meji
   • Ko lo awoṣe iyaworan lọtọ - awoṣe ibi-afẹde nikan funrararẹ
   • O ṣiṣẹ fun awọn iṣẹ-ṣiṣe itumọ nikan
   • O ṣe iwọn awoṣe nigbagbogbo
2. Ọna nomba wo ni o ṣe atilẹyin wiwa wiwa ori ori?
   • Isosile ite
   • Fourier yipada
   • K-tumo si iṣupọ
   • Jacobi (ti o wa titi-ojuami) aṣetunṣe
3. Kí ni àwọn ẹ̀ka ọ̀tọ̀ọ̀tọ̀ méjì tí wọ́n ń ṣiṣẹ́ ní ìṣísẹ̀ kọ̀ọ̀kan?
   • Ẹka ikẹkọ ati ẹka idanwo kan
   • Ohun kooduopo ati ki o kan decoder
   • Ẹka ti n wo ori ati ẹka ijẹrisi kan
   • Awoṣe awoṣe ati awoṣe afojusun kan
4. Kini o wa ni ipamọ ninu 'n-gram pool'?
   • Awọn itọsẹ olona-àmi ti o ṣeeṣe ti a rii lakoko awọn aṣetunṣe ti o jọra
   • Itan iwiregbe olumulo
   • Awọn iwọn awoṣe
   • Awọn iboju iparada
5. What supports correctness when lookahead decoding commits a guessed n-gram prefix?
   • Using the same model makes every unverified guess correct
   • Every candidate in the n-gram pool must be committed
   • Verification against the target's greedy predictions or an appropriate distribution-preserving sampling procedure
   • A larger window proves the generated statements are true
6. What resource tradeoff is important when deploying lookahead decoding on already busy hardware?
   • Candidate generation uses no compute or memory
   • Additional parallel work may offset the benefit of fewer sequential passes
   • Every larger window must lower wall-clock latency
   • The target weights are automatically compressed to zero size
7. Why should a lookahead candidate remain provisional until target verification?
   • Parallel iteration guesses can differ from the target's valid continuation
   • Every cached n-gram has already been proven correct by storage alone
   • The lookahead branch permanently changes the target weights
   • Verification is needed only to determine the output filename
8. Does a standard training-free lookahead iteration update the target model's learned weights?
   • Yes; each candidate applies a gradient update to every parameter
   • Yes; verified n-grams become a new training dataset immediately
   • Only the tokenizer's vocabulary is retrained at every step
   • No; it changes candidate token states and verifies outputs while keeping the model weights fixed