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OpenAI Quiz sur les modèles de raisonnement o1 et o3

Examine le comportement des jetons de raisonnement, les demandes de formation par apprentissage par renforcement, les compromis de latence et les preuves du modèle OpenAI à l'échelle de la version.

Chemins de guidage associésModèles de raisonnement Openai o1 et O3
Question 1 de 8

What does OpenAI’s o-series documentation say the models use before returning a final answer?

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  1. What does OpenAI’s o-series documentation say the models use before returning a final answer?
    • Internal reasoning tokens that are not necessarily exposed as a full user-visible transcript
    • A guaranteed step-by-step explanation shown to every user
    • A separate web search for every response
    • No extra computation beyond a standard model
  2. What does OpenAI say is part of the o1 series training approach?
    • Only retrieval from a web index
    • Reinforcement learning to perform complex reasoning
    • Only next-token pretraining with no post-training
    • Only human-written chain-of-thought imitation
  3. What does inference-time compute scaling mean for reasoning models?
    • Each additional reasoning token guarantees a better answer
    • The model retrains its weights after each question
    • More reasoning computation may improve some tasks, with added cost and latency
    • Extra computation removes the need to evaluate model versions
  4. How should a reported benchmark score for an OpenAI reasoning model be interpreted?
    • As a guarantee of performance on every user task
    • As a result that applies to all future o-series versions
    • As independent of the benchmark harness
    • As tied to the named model version and evaluation protocol
  5. Why might internal reasoning tokens affect API cost even when users do not see them?
    • They count toward output usage and context limits
    • They are removed from all billing and token accounting
    • They are stored only in the user’s browser
    • They replace all visible answer tokens
  6. Quel est l’inconvénient typique des modèles de raisonnement de type o1/o3 ?
    • Ils ne peuvent répondre à aucune question
    • Coût plus élevé et réponses plus lentes pour les requêtes difficiles
    • Ils ne font jamais d'erreurs
    • Ils ne travaillent qu'en anglais
  7. What does a reasoning-effort setting influence in an API request?
    • The model’s training data after deployment
    • Whether the model can access every third-party tool
    • The reasoning budget allocated to the request
    • The benchmark score reported for earlier versions
  8. How should developers check which OpenAI reasoning model is available and its current API behavior?
    • Assume every o-series model has identical endpoints
    • Use an old benchmark article as an API specification
    • Infer availability from the model family name alone
    • Consult the current official model and reasoning documentation for that version