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What Is a Reasoning Model? | IBM

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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra for machine learning Data visualization for machine learning Uncertainty quantification Bias variance tradeoff Bayesian Statistics Singular value decomposition Overview Feature selection Feature extraction Vector embedding Latent space Principal component analysis Linear discriminant analysis Upsampling Downsampling Synthetic data Data leakage Overview Linear regression Lasso regression Ridge regression State space model Time series Autoregressive model Overview Decision trees K-nearest neighbors (KNNs)Naive bayes Random forest Support vector machine Logistic regression Overview Boosting Bagging Gradient boosting Gradient boosting classifier Overview Transfer learning Overview Overview K means clustering Hierarchical clustering A priori algorithm Gaussian mixture model Anomaly detection Overview Collaborative filtering Content based filtering Overview Reinforcement learning human feedback Deep reinforcement learning Overview Overview Backpropagation Encoder-decoder model Recurrent neural networks Long short-term memory (LSTM)Convolutional neural networks Overview Attention mechanism Grouped query attention Positional encoding Autoencoder Mamba model Graph neural network Overview Generative model Generative AI vs. predictive AI Overview Reasoning models Small language models Instruction tuning LLM parameters LLM temperature LLM benchmarks LLM customization LLM alignment Tutorial: Multilingual LLM agent Diffusion models Variational autoencoder (VAE)Generative adversarial networks (GANs)Overview Vision language models Tutorial: Build an AI stylist Tutorial: Multimodal AI queries using Llama Tutorial: Multimodal AI queries using Pixtral Tutorial: Automatic podcast transcription with Granite Tutorial: PPT AI image analysis answering system Overview GraphRAG Tutorial: Build a multimodal RAG system with Docling and Granite Tutorial: Evaluate RAG pipline using Ragas Tutorial: RAG chunking strategies Tutorial: Graph RAG using knowledge graphs Tutorial: Inference scaling to improve multimodal RAG Overview Vibe coding Visit the 2025 Guide to AI Agents Overview LLM training Loss function Training data Model parameters Overview Gradient descent Stochastic gradient descent Hyperparameter tuning Learning rate Overview Parameter efficient fine tuning (PEFT)LoRA Tutorial: Fine tuning Granite model with LoRA Regularization Foundation models Overfitting Underfitting Few shot learning Zero shot learning Knowledge distillation Meta learning Data augmentation Catastrophic forgetting Overview Scikit-learn XGboost PyTorch Overview AI lifecyle AI inference Model deployment Machine learning pipeline Data labeling Model risk management Model drift AutoML Model selection Federated learning Distributed machine learning AI stack Overview Natural language understanding Overview Sentiment analysis Tutorial: Spam text classifier with PyTorch Machine translation Overview Information retrieval Information extraction Topic modeling Latent semantic analysis Latent Dirichlet Allocation Named entity recognition Word embeddings Bag of words Intelligent search Speech recognition Stemming and lemmatization Text summarization Conversational AI Conversational analytics Natural language generation Overview Image classification Object detection Instance segmentation Semantic segmentation Optical character recognition Image recognition Visual inspection Dave Bergmann large language model (LLM)fine-tuned training models machine learning training data artificial general intelligence (AGI)DeepSeek-R1 IBM Granite IBM Privacy Statement “System 1” and “System 2” thinking chain of thought prompting inference scaling self-supervised supervised instruction-tuned Reinforcement learning from human feedback (RLHF)loss function DeepSeek-R1 model backpropagated synthetic training data Knowledge distillation few-shot Go to episode ArenaHard Alpaca-Eval-2 thought preference optimization (TPO)context window API Upcoming webinar - May 19, 2026 Designing customer engagement: AI workflows that solve problems, not frustrate customers Learn how AI agents reduce service friction, fit into real workflows, and help teas deliver faster, more engaging customer experiences. Join now Guide The CEO's guide to model optimization Learn how to continually push teams to improve model performance and outpace the competition by using the latest AI techniques and infrastructure. Read the guide Training watsonx® Developer Hub Support your next project with some of our most commonly used capabilities. Get started and learn more about the supported models that IBM provides. Get started Report A differentiated approach to AI foundation models Explore the value of enterprise-grade foundation models that provide trust, performance and cost-effective benefits to all industries. Read the report Ebook Unlock the power of generative AI and ML Learn how to incorporate generative AI, machine learning and foundation models into your business operations for improved performance. Read the ebook Insight How IBM is tailoring generative AI for enterprises Learn how IBM is developing generative foundation models that are trustworthy, energy efficient and portable. Read the insight Explore Granite Explore AI solutions Explore AI services Discover watsonx.ai Explore IBM Granite AI models "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity,""Introducing OpenAI o1-preview,""From System 1 to System 2: A Survey of Reasoning Large Language Models,""Large Language Models are Zero-Shot Reasoners,""Show Your Work: Scratchpads for Intermediate Computation with Language Models,""Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters,""Let's Verify Step by Step,""Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations,""s1: Simple test-time scaling,""Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models,""STaR: Bootstrapping Reasoning With Reasoning,""Reinforced Self-Training (ReST) for Language Modeling,""Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs,""The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks,""Inverse Scaling in Test-Time Compute,""Bringing reasoning to Granite,""Claude 3.7 Sonnet and Claude Code,""Generative AI on Vertex AI: Thinking,""Reasoning models don't always say what they think,"

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