mlsanityData to 7 Oct 2026

Focused ReAct: Improving ReAct through Reiterate and Early Stop

arXiv 2410.10779 ↗First posted 14 Oct 2024cs.AIcited by 3

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Large language models (LLMs) have significantly improved their reasoning and decision-making capabilities, as seen in methods like ReAct. However, despite its effectiveness in tackling complex tasks, ReAct faces two main challenges: losing focus on the original question and becoming stuck in action loops. To address these issues, we introduce Focused ReAct, an enhanced version of the ReAct paradigm that incorporates reiteration and early stop mechanisms. These improvements help the model stay focused on the original query and avoid repetitive behaviors. Experimental results show accuracy gains of 18% to 530% and a runtime reduction of up to 34% compared to the original ReAct method.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 6 of its 7 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

6 of 7 references.

References · 6

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  4. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  5. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
  6. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impactcited by 9
  2. 2025Data Augmentation Through Random Style Replacementcited by 9
  3. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models
  2. 2024StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking
  3. 2026Learning to Reason and Use Tools through Unsupervised Fine-Tuning in Task-Oriented Dialog Systems
  4. 2025Adaptive Distraction: Probing LLM Contextual Robustness with Automated Tree Search
  5. 2024Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style
  6. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
  7. 2024Exploring Advanced Large Language Models with LLMsuite
  8. 2026Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation