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February 2026

ReAct: Synergizing Reasoning and Acting in Language Models ↗

paper

arXiv · October 2022

Interleaves chain-of-thought reasoning traces with concrete actions in an observe-think-act loop
Outperforms pure reasoning (chain-of-thought) and pure acting (action-only) on knowledge-intensive tasks by grounding thoughts in tool outputs
Foundational pattern behind most modern agent frameworks — the shell-like “read, eval, print” loop applied to LLMs

Toolformer: Language Models Can Teach Themselves to Use Tools ↗

paper

arXiv · February 2023

Demonstrates that language models can learn when and how to call external tools (calculator, search, calendar) through self-supervised training
The model inserts API calls into its own text generation when doing so reduces perplexity — tool use emerges from utility, not instruction
Shows that tool augmentation is a natural extension of next-token prediction, not a bolted-on capability