arXiv 2610.16523 · cs.AI · 2026-10-12
EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents
Min Cai, Dan Zhang, Gao Xing, Libin Ma, Zhaohui Xu, Xi Ye
University of Alberta
In latest · #312 Added on 2026-10-25
One-line takeaway
EAU co-evolves environment, agent and simulated user during training: a meta-agent monitors trajectories, tool traces and rewards to fix inconsistent user simulators, unreliable tools and wrong verifiers instead of treating them as fixed.
EAU 让环境、agent 和模拟用户在训练中共同进化：元 agent 监控轨迹、工具记录和奖励，修正不稳定的用户模拟器、出错的工具和验证器，而不是把它们当作固定不变。
Read on arXiv PDF Hugging Face discussion Code Project page
Topics & disciplines
More on Agents
- Raven: The Harness of Harnesses for Composable Agentic Intelligence ▲748
- False Frontiers: Diagnosing and Mitigating Co-Cheating in Self-Evolving Search Agents ▲642
- Omni-IO Skills: Harnessing Your Agent Omni-Native ▲485
- BizAgentBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business ▲388
- Player-1: Learning to Play from Synthetic Experience ▲347
More on Reinforcement learning
- The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation 2026-09-29 · ▲511
- Scaling Properties of Same-Family On-Policy Distillation 2026-09-26 · ▲327
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models 2026-10-08 · ▲325
- Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation 2026-10-15 · ▲214
- Agent-Reflex: Teaching Language Agents to Act Reflectively 2026-10-13 · ▲214
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-26.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.