//Reasoning Models
How AI Agents Learn to Reason
Tags : Agent TrainingAI agent developmentAI agentsDeepSeek R1GRPOLLM TrainingMachine LearningModel AlignmentProcess Reward ModelsReasoning ModelsReinforcement LearningReward FunctionsRLHFRLVRVerifiable Rewards
The trustworthiness of AI agents deployed in production depends on the rigor of their training methodologies. Traditionally, reinforcement learning from human feedback (RLHF) has been used, where human evaluators rank model responses to guide preferred behaviors. However, human preference is slow, costly, subjective, and does not guarantee correctness. Reinforcement learning from verifiable rewards (RLVR) offers.. Read more
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Reasoning Models and Test-Time Compute: Letting AI Agents Think Before They Act
Tags : agent architectureAgent DevelopmentAI agentsAI inferenceAI reasoningChain of ThoughtExtended Thinkinginference computelanguage modelsreasoning engineReasoning ModelsReinforcement Learningtest time scalingTest-Time Computethinking models
Reasoning models and test-time compute let AI agents generate a hidden chain of thought — planning, checking assumptions, and self-correcting — before they take action. A practical look at why smarter inference beats bigger models, the latency and cost trade-offs, and how to put reasoning agents into production without overspending.
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