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vllbc 收录于 Agent Reading
 2025-08-08  约 258 字   预计阅读 1 分钟    次阅读  

tool-use

通过RL提高模型调用工具的能力。 ### Search-R1:Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

WebThinker:Empowering Large Reasoning Models with Deep Research Capability

ZEROSEARCH:Incentivize the Search Capability of LLMs without Searching

AGENTIC REINFORCED POLICY OPTIMIZATION

Search and Refine During Think:Autonomous Retrieval - Augmented Reasoning of LLMs

planning

通过RL提高长程规划能力,一般都会针对信用分配问题进行解决。 ### RLVMR:Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents

Group-in-Group Policy Optimization for LLM Agent Training

Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning

参考

# 2025年大模型agent rl训练多轮planning技术TORL,ToolRL, RAGEN,OTC,SkyRL-v0, GiGPO,Tool-N1 ,ARTIST, ZeroTIR, GRPO

更新于 2025-08-08
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