reasoning models
Applied AI interview questions tagged reasoning models, across every topic.
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Concepts behind "reasoning models"
The curriculum that explains the ideas these questions test.
Core
Inference-Time Compute and Reasoning ModelsInference-time (test-time) compute is the idea that spending more computation at generation, longer chains of thought, sampling multiple attempts, or search, reliably improves answers on hard problems, a scaling axis distinct from making the model bigger. Reasoning models (o1/R1-style) are trained, often via RL on verifiable rewards, to produce long internal reasoning and exploit this. Applied-AI interviews probe it because it changed how hard problems are solved and introduced a real latency/cost trade-off: route easy queries to fast models, reserve reasoning models for genuinely hard ones.🧠 Foundations of LLMs & GenAISign in
Core
Small vs Large Models and RoutingBigger is not always better in production: small models are far cheaper and faster, and for many tasks they are good enough, especially when fine-tuned or given retrieval. The mature pattern is routing, send easy queries to a small/cheap model and reserve large or reasoning models for genuinely hard ones, often with a cascade that escalates on low confidence. Applied-AI interviews probe it because picking and routing models is where most of the cost and latency budget is won or lost.🧠 Foundations of LLMs & GenAISign in
Advanced
Policy Optimization: PPO and GRPOPPO and GRPO are the reinforcement-learning algorithms that optimize an LLM against a reward, the RL step in RLHF and in training reasoning models. PPO is the established workhorse, updating the policy in small, clipped steps to stay stable; GRPO (used by DeepSeek-R1) drops PPO's separate value network and instead normalizes rewards within a group of samples, which is simpler and cheaper for LLMs. Applied-AI interviews probe it because it explains how alignment and reasoning training actually run, and why RL on verifiable rewards scales.🧠 Foundations of LLMs & GenAI🔒 Premium