arXiv (OAI Expanded)
open access
SRPO: Setwise Relative Policy Optimization for Multi-Agent Systems
Multi-agent systems enable complex reasoning and tool use by coordinating agents that divide roles and refine candidate solutions. Existing methods typically update individual agent responses or treat a complete trajectory as one training example. However, these methods may produce misleading policy
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Network Interventions: Targeting Agents or Targeting Links?#973068
- Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization#1216207
- SRPO: Setwise Relative Policy Optimization for Multi-Agent LLMs#985805
- AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems#985913
- PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning#394456
Record · ID 1319158
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive