# ReAct — reasoning + acting

Yao et al., "ReAct: Synergizing Reasoning and Acting in Language Models"
ICLR 2023. arXiv:2210.03629

The interesting one. Sits right between two lines of work I care about:

1. the chain-of-thought reasoning line (Wei et al.) — reasoning, but no contact
   with the outside world
2. the tool-use line (Toolformer etc.) — actions, but no explicit reasoning about
   when/why to act

ReAct interleaves them: Thought → Action → Observation → Thought → ...
The model reasons about what it knows, takes an action (e.g. search Wikipedia),
reads the observation, and keeps reasoning.

> "reasoning traces help the model induce, track, and update action plans as well
> as handle exceptions, while actions allow it to interface with external sources"

Evals: HotpotQA + Fever (knowledge), ALFWorld + WebShop (decision making).
Key claim: ReAct beats CoT-only on factual tasks because grounding in retrieved
text reduces hallucination. CoT alone makes things up confidently.

my take: this is basically the blueprint for every agent framework that came after.
Reflexion literally builds its self-reflection loop on top of ReAct-style agents.

TODO-VERIFY: check the exact WebShop success-rate numbers before citing anywhere.
