2210.03629.pdf
arxiv.org · 7,323 words · saved by 1 readers
N/A
Published as a conference paper at ICLR 2023 R E A C T : S YNERGIZING R EASONING AND ACTING IN L ANGUAGE M ODELS Shunyu Yao∗*,1 , Jeffrey Zhao2 , Dian Yu2 , Nan Du2 , Izhak Shafran2 , Karthik Narasimhan1 , Yuan Cao2 1 Department of Computer Science, Princeton University…
related reading
- ReAct: Synergizing Reasoning and Acting in Language Modelsai.googleblog.com
- [2201.11903] Chain of Thought Prompting Elicits Reasoning in Large Language Modelsarxiv.org
- [2412.06769] Training Large Language Models to Reason in a Continuous Latent Spacearxiv.org
- Language Models can Solve Computer Tasksarxiv.org
- [2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasksarxiv.org
- 1b44b878bb782e6954cd888628510e90-Paper-Conference.pdfproceedings.neurips.cc
- [2603.07267] How to Steal Reasoning Without Reasoning Tracesarxiv.org
- 2025.acl-long.896.pdfaclanthology.org
- [2210.13382] Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Taskarxiv.org
- Reasoning Models Reason Well, Until They Don'tarxiv.org
- 2401.10020.pdfarxiv.org
- 2307.12950.pdfarxiv.org