ai-engineering-from-scratch/phases/14-agent-engineering/03-reflexion-verbal-rl/quiz.json at main · zhaozhiqiangjianming-ship-it/ai-engineering-from-scratch · GitHub

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{
  "lesson": "03-reflexion-verbal-rl",
  "title": "Reflexion: Verbal Reinforcement Learning",
  "questions": [
    {
      "stage": "pre",
      "question": "What does Reflexion replace in standard reinforcement learning?",
      "options": [
        "Reward shaping with a constant reward",
        "Gradient updates with natural-language reflections stored between trials",
        "Policy networks with random search",
        "Episodic memory with parametric memory"
      ],
      "correct": 1,
      "explanation": "Reflexion uses natural-language reflections in episodic memory instead of weight updates."
    },
    {
      "stage": "pre",
      "question": "What three components define a Reflexion system?",
      "options": [
        "Planner, Worker, Solver",
        "Actor, Evaluator, Self-Reflector",
        "Generator, Critic, Optimizer",
        "Selector, Expander, Backpropagator"
      ],
      "correct": 1,
      "explanation": "Reflexion factors the agent into an Actor that runs trajectories, an Evaluator that scores them, and a Self-Reflector that writes lessons."
    },
    {
      "stage": "check",
      "question": "Which evaluator type uses an external binary signal like a unit test or a known correct answer?",
      "options": [
        "Heuristic",
        "Scalar",
        "Self-evaluated",
        "Vote-based"
      ],
      "correct": 1,
      "explanation": "Scalar evaluators read pass/fail signals from ground truth (ALFWorld success, HumanEval tests)."
    },
    {
      "stage": "check",
      "question": "Why is self-evaluation a weaker signal than scalar feedback?",
      "options": [
        "It is slower to compute",
        "The model judging itself has no external grounding so it can rubber-stamp its own answer",
        "It always requires a larger model",
        "It cannot run on tools"
      ],
      "correct": 1,
      "explanation": "Self-eval lacks an external check, so a confident hallucination scores well; pair it with tool-grounded verification."
    },
    {
      "stage": "check",
      "question": "Which case does the lesson list as a place where Reflexion does NOT help?",
      "options": [
        "Code generation where tests can score",
        "An external transient failure like the network being down",
        "ALFWorld navigation tasks",
        "HotpotQA multi-hop questions"
      ],
      "correct": 1,
      "explanation": "Reflecting on a transient external outage produces a reflection that does not help future runs."
    },
    {
      "stage": "post",
      "question": "What is memory rot in the Reflexion pattern?",
      "options": [
        "Losing reflections when the process restarts",
        "Episodic buffer fills with obsolete or wrong reflections and slows or biases future trials",
        "Reflections get encrypted by the provider",
        "The reflection prompt exceeds the context window"
      ],
      "correct": 1,
      "explanation": "Accumulated stale or wrong reflections degrade behavior; mitigate with compaction or TTL."
    },
    {
      "stage": "post",
      "question": "Which production pattern is the lesson's clearest match for Reflexion?",
      "options": [
        "Cursor's apply-edits flow",
        "Claude Code's CLAUDE.md learnings prepended to future sessions",
        "OpenAI's batch API",
        "Anthropic's prompt caching"
      ],
      "correct": 1,
      "explanation": "CLAUDE.md learnings, pro-workflow's learn-rule, and Letta's sleep-time compute all externalize the episodic reflection buffer."
    }
  ]
}