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Reasoning & Chain-of-Thought Models

Chain-of-thought prompting, tree-of-thought search, test-time compute scaling, verification, tool-augmented reasoning, and the frontier of AI reasoning
Co-Created by Kiran Shirol and Claude
Topics Chain-of-Thought Test-Time Compute Verification Tree Search Tool Use Benchmarks
home Learning Portal play_arrow Start Learning summarize Key Insights dictionary Glossary 8 chapters · 2 sections
Section 1

Foundations — How LLMs Reason

The reasoning gap, chain-of-thought prompting, tree search, and test-time compute scaling.
1
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The Reasoning Gap
Why LLMs struggle with logic, math, and multi-step problems. System 1 vs System 2 thinking in AI.
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2
route
Chain-of-Thought Prompting
Wei et al. (2022), zero-shot CoT, self-consistency, and why “let’s think step by step” works.
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3
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Tree-of-Thought & Search
Yao et al. (2023), BFS/DFS over reasoning paths, Monte Carlo Tree Search for LLMs.
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Test-Time Compute Scaling
OpenAI o1/o3, thinking tokens, compute-optimal inference, and scaling laws for reasoning.
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Section 2

Advanced — Verification, Tools & Future

Process reward models, tool-augmented reasoning, benchmarks, and the future of AI reasoning.
5
verified
Verification & Reward Models
PRMs vs ORMs, step-level verification, Monte Carlo labels, and verification-guided search.
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6
build
Tool-Augmented Reasoning
Code interpreters, calculators, retrieval for reasoning, Toolformer, and PAL.
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Benchmarks & Evaluation
GSM8K, MATH, ARC-AGI, GPQA, HumanEval, and measuring reasoning vs memorization.
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The Future of Reasoning
Open-source reasoning models (DeepSeek-R1, Qwen QwQ), hybrid neuro-symbolic, and what’s next.
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