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Training Reasoning Models: RLVR, PRM vs ORM
Reasoning models like o1 and R1 are not just bigger instruct models: they are trained with reinforcement learning where the reward comes from checking whether the final answer is correct, which teaches the model to generate long internal chains of thought. This page covers RL with verifiable rewards (and GRPO specifically), the split between process reward models that score each step and outcome reward models that score only the answer, and how that choice shapes test-time search. Applied AI interviews probe it to see if you understand where the reasoning ability actually comes from.
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LLM & GenAI FundamentalsWhat is RLVR (reinforcement learning with verifiable rewards), and why does it work for reasoning models?→LLM & GenAI FundamentalsExplain PPO and GRPO for LLM alignment. Why did GRPO drop the value model?→LLM & GenAI FundamentalsWalk through GRPO's implementation: group sampling, advantage normalization, and the failure modes you watch for.→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→System Design for AI in ProductionDesign a fraud-detection system that uses LLMs (beyond a classic ML classifier).→LLM & GenAI FundamentalsHow are reasoning models (o1/R1-style) trained, and what is test-time compute scaling?→
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