arXiv (All)
open access
Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a fixed scenario pool collected before learning begins. This creates a distributional bottleneck: as the agent improves, th
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
- Stable and Efficient Real-World Online VLA Post-Training via Asynchronous Replay-Anchored Policy Improvement#1037571
- Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit#423993
- Comparative Approaches to Agent Retrieval over Large Skill Libraries#432572
- RotVLA: Rotational Latent Action for Vision-Language-Action Model#1077889
- Looped Diffusion Language Models#224495
Record · ID 1076265
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