A SURVEY OF FRONTIERS IN LLM REASONING: INFERENCE SCALING, LEARNING TO REASON, AND AGENTIC SYSTEMS

Summary from the TLDR AI newsletter

LLM reasoning methods fit into two dimensions: regimes (inference-time vs. training-based) and architectures (standalone vs. agentic systems). There's been a shift from inference scaling like the original o1 to learning-to-reason approaches like DeepSeek-R1 as part of the transition to agentic workflows.