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About this interview
A technical interview on Reinforcement Learning, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain the RL framework: agent, environment, state, action, reward, and policy
Compare model-based vs model-free RL and value-based (Q-learning, DQN) vs policy gradient methods
Describe the actor-critic architecture and the role of the value network in reducing variance
Explain how RLHF connects RL to language model alignment at a high level
Topics covered
RL FrameworkExploration vs ExploitationValue-Based vs Policy GradientModel-Based vs Model-FreeValue FunctionsQ-LearningDeep Q-NetworksPolicy Gradient MethodsActor-CriticAdvanced Policy OptimizationTD vs Monte CarloBandits and Applied RLRLHF and LLM AlignmentPractical RL Challenges
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“How would you explain the core reinforcement learning framework to someone who has only done supervised learning? What are the key pieces — agent, environment, state, action, reward — and how do they relate to each other?
“Walk me through how Q-learning works. What is the update rule, and why is it considered an off-policy algorithm?
“What problem does TRPO try to solve that vanilla policy gradient methods don't address? What constraint does TRPO enforce, and why does that constraint matter for training stability?