mlsanityData to 7 Oct 2026

ParaAgent: Reinforcing Parallel Acting in Open-World Tool Environments

arXiv 2609.33618 ↗First posted 27 Sep 2026cs.AI cs.CLcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents also fits: General LLM agent frameworks and benchmarks

Abstract (arXiv)

Language model agents are increasingly deployed in open-world tool environments, which require balancing exploring unknown capabilities and exploiting known ones. Existing methods face a performance-efficiency tradeoff: they either rigidly decouple exploration and execution or interleave them without coordination. We argue that the key lies not in whether to decouple or interleave them, but in how to coordinate them across granularities. We introduce ParaAct, a structured parallel-action loop that combines phase-level Exploration rightleftharpoons Execution with action-level parallelism. To learn this loop, ParaAgent combines multi-agent cold-start demonstrations with reinforcement learning under multi-level advantage decoupling, making planning structure explicit and supervising it with step-, phase-, and trajectory-level rewards. Learning is supported by our ToolEnv, a scalable simulator grounded in 50,011 realistic tool interfaces. On two open-world tool benchmarks, ParaAgent-4B achieves the best average success among all baselines, including GPT-4.1 systems, with the largest gains on multi-tool tasks. Behavioral analyses show that these gains stem from this action organization, highlighting its importance for capable and efficient open-world agents.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 43 of its 50 references that are in the map. First posted on arXiv 27 Sep 2026.

Builds on

43 of 50 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Simulating Environments with Reasoning Models for Agent Training3 Nov
  2. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Survey2 Sep
  3. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning24 Apr
  4. 2025ToolRL: Reward is All Tool Learning Needs16 Apr
  5. 2025ReTool: Reinforcement Learning for Strategic Tool Use in LLMs15 Apr
  6. 2023Reflexion: Language Agents with Verbal Reinforcement Learning20 Mar

Other references · 37

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2025Qwen3 Technical Reportcited by 8,195
  5. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  6. 2024Qwen2.5 Technical Reportcited by 4,713
  7. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 1,980
  8. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  9. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  10. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  11. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 574
  12. 2023AgentTuning: Enabling Generalized Agent Abilities for LLMscited by 319
25 more
  1. 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 268
  2. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
  3. 2024Tool Learning with Large Language Models: A Surveycited by 221
  4. 2025WebSailor: Navigating Super-human Reasoning for Web Agentcited by 218
  5. 2024ToolACE: Winning the Points of LLM Function Callingcited by 204
  6. 2024APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasetscited by 188
  7. 2026GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimizationcited by 175
  8. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 174
  9. 2025WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learningcited by 162
  10. 2024StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Modelscited by 129
  11. 2024Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Modelscited by 97
  12. 2025Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RLcited by 75
  13. 2025DeepAgent: A General Reasoning Agent with Scalable Toolsetscited by 69
  14. 2026Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineeringcited by 68
  15. 2025TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environmentscited by 61
  16. 2025Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Modelscited by 46
  17. 2025From Word to World: Can Large Language Models be Implicit Text-based World Models?cited by 40
  18. 2024Towards Completeness-Oriented Tool Retrieval for Large Language Modelscited by 26
  19. 2024Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Taskscited by 23
  20. 2025Multi-Agent Simulator Drives Language Models for Legal Intensive Interactioncited by 22
  21. 2024Enhancing Tool Retrieval with Iterative Feedback from Large Language Modelscited by 17
  22. 2025Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocationcited by 16
  23. 2025Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environmentscited by 13
  24. 2024Improving Tool Retrieval by Leveraging Large Language Models for Query Generationcited by 11
  25. 2026ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Executioncited by 7

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  4. 2026Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
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  7. 2025Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing
  8. 2026OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction