> ML_LITERATURE // YAO-2022-REACT-SYNERGIZING-REASONING-AND-ACTING_v1.0
ReAct: Synergizing Reasoning and Acting in Language Models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao · International Conference on Learning Representations (ICLR) (2022)
algorithm2022industry-standardthirdPartyReproduced
Principal Contribution
Interleaved reasoning traces ("Thought") with task-specific actions ("Action") and environment feedback ("Observation"), creating the paradigm for autonomous LLM agents.
Operational Relevance
Directly guides architectural decisions, alignment strategy, and serving infrastructure for task-autonomous-agents, task-text-generation.
Assumptions
- Empirical distribution regularity holds and target domain adheres to pretraining linguistic/visual support
Limitations
- Resource scaling, inference memory requirements, and alignment robustness vary with model size and hardware topology
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: