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Core
Inference-Time Compute and Reasoning Models
Inference-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 use this. AI, ML, and GenAI interviews probe it because it changed how hard problems get solved and introduced a real latency/cost trade-off: route easy queries to fast models, reserve reasoning models for genuinely hard ones.
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LLM & GenAI FundamentalsHow are reasoning models (o1/R1-style) trained, and what is test-time compute scaling?→LLM & GenAI FundamentalsWhat are small language models (SLMs) and reasoning models (LRMs), and when do you choose each?→LLM & GenAI FundamentalsWalk through GRPO's implementation: group sampling, advantage normalization, and the failure modes you watch for.→ML Infrastructure & GPUsReasoning models made your traffic decode-heavy: 30k thinking tokens per request. What changes in your serving stack?→LLM & GenAI FundamentalsWhat is RLVR (reinforcement learning with verifiable rewards), and why does it work for reasoning models?→LLM & GenAI FundamentalsWhen should a request hit a reasoning model, and how do you stop it from overthinking?→
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