Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Ara-Play Fine-Tuning adanwo

Explore self-play in Go and language-model training, compare AlphaGo Zero with AlphaZero, and examine fixed-data signals and synthetic-data risks.

Jẹmọ itọsọna awọn ọnaAra Play Fine Tuning
Ibeere 1 ti 8

Eto olokiki wo ni o de ere superhuman Go ni lilo ere ti ara ẹni nikan ko si awọn igbasilẹ ere eniyan?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Eto olokiki wo ni o de ere superhuman Go ni lilo ere ti ara ẹni nikan ko si awọn igbasilẹ ere eniyan?
    • AlphaGo Zero
    • Blue Jin
    • Watson
    • GPT-2
  2. Ni SPIN, kini ipa ti 'alatako' ti awoṣe gbọdọ lu?
    • A lọtọ eda eniyan-oṣiṣẹ ere awoṣe
    • Aaye ayẹwo iṣaaju ti awoṣe naa ati awọn idahun ti ipilẹṣẹ
    • A ID ọrọ monomono
    • Awọn tokenizer
  3. Kini anfani-ṣiṣe data akọkọ ti ṣiṣe-ṣere ti ara ẹni daradara?
    • O nilo iyasọtọ tuntun dataset eniyan ni yika kọọkan
    • O ṣiṣẹ nikan pẹlu data ailopin
    • O le jade awọn anfani diẹ sii lati inu data ti o wa titi laisi awọn aami eniyan tuntun
    • Ko nilo awoṣe rara
  4. Ninu ibi-afẹde ikẹkọ SPIN, kini awoṣe titari lati ṣe?
    • Ṣe ayanfẹ awọn idahun ti ipilẹṣẹ tirẹ ju awọn itọkasi eniyan lọ
    • Foju gbogbo data eniyan
    • Ṣeto eto idanwo naa sori
    • Fi o ṣeeṣe ti o ga julọ si awọn idahun itọkasi eniyan ju awọn abajade ti ara rẹ ṣaaju
  5. Which pair of games did the AlphaZero paper demonstrate beyond its Go experiments?
    • Chess and shogi
    • Poker and bridge
    • Checkers and Othello
    • Atari and StarCraft
  6. What risk did researchers report when language models are recursively trained on generated data?
    • Model collapse, with loss of diversity and representation of the original distribution
    • Guaranteed monotonic improvement at every iteration
    • Automatic prevention of distribution shift
    • A higher count of verified human labels
  7. What search component does AlphaGo Zero use alongside self-play?
    • A nearest-neighbor lookup over human game records
    • A language-model tokenizer
    • Monte Carlo tree search guided by a policy and value network
    • A fixed list of opening moves
  8. Why can automatic checkers be useful in self-play for math or code tasks?
    • They guarantee that a model never learns incorrect strategies
    • They replace the need for a task specification
    • They make all generated examples independent of the model
    • They can verify generated solutions without requiring a new human label for each attempt