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Reinforcement Learning · section 12 of 12

Interview scenarios

5 interview questions on interview scenarios, each answered in full. Free to read, no account needed.

Interview: 'design an RL system for X' — what's your framework?

medium
  • Structure: (1) Define MDP: state (features observable), action (decision), reward (business KPI), horizon.
  • (2) Identify challenges: sparse / delayed rewards, safety, sample efficiency.
  • (3) Choose approach: online RL vs offline RL vs BC vs bandit vs supervised.
  • (4) Design reward + KL / constraint (avoid hacking).
  • (5) Simulation + safety validation before deployment.
  • (6) A/B test + monitor drift.
  • Show tradeoffs at each step.
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Interview: 'when should you NOT use RL?'

medium
  • (1) Supervised data + clear labels available — SL is faster + safer.
  • (2) Task has one shot (no sequential decisions).
  • (3) Reward hard to specify — SL on demos may be better.
  • (4) Cannot simulate / afford exploration + no logged data.
  • (5) Business needs interpretability RL lacks.
  • (6) Costly failures + can't validate before deployment.
  • Rule: RL is expensive to get right; use only when the sequential / decision structure genuinely warrants it.
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Interview: 'you're given 100 hours of expert data + can simulate — how do you train an agent?'

hard
  • (1) BC first: quick baseline from 100 hours of demos → benchmark.
  • (2) Warm-start RL from BC weights → faster than from scratch.
  • (3) In sim: PPO or SAC with domain randomization; expert data as replay seed.
  • (4) Iterative: sim-fine-tune → deploy → collect new demos on failure cases → SFT round.
  • (5) HITL: BC + RL fine-tune with expert intervention on failure states (DAgger-style).
  • (6) Monitor gap between BC + RL policies; if RL not exceeding, reward is misspecified.
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Interview: 'DQN vs PPO vs SAC vs DPO — which for what?'

medium
  • DQN: discrete actions, Q-learnable, replay works → Atari, tabular-like.
  • PPO: general workhorse, on-policy, LLMs (RLHF), games — reliable + tunable.
  • SAC: continuous control, sample-efficient, robotics — max-entropy + off-policy.
  • DPO: preference-based LLM alignment — simplest path from preference data.
  • Rule: DQN discrete-only; PPO everywhere; SAC continuous + sample-eff; DPO for LLM preferences.
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Interview: 'summarize when RL wins in production.'

medium
  • RL wins when: (1) task has sequential decisions with delayed reward that SL / bandit can't capture.
  • (2) You can simulate cheaply or have massive logged interaction data (offline RL).
  • (3) Optimizing long-term outcomes (retention, LTV, revenue).
  • (4) Reward signal is clean + auto-computable (verifier tasks: math, code).
  • (5) You need superhuman / above-demo behavior.
  • Rule: RL is a targeted tool.
  • Most 'RL problems' are better as bandit or SL — audit before starting.
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Practise Reinforcement Learning

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