arXiv (All)
open access
Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
This work presents a general framework for training large language models (LLMs) to "Connect the Dots" (CoD), a meta-capability required by long-lifecycle agents: as an LLM-based AI agent gets deployed in an environment, it solves a long sequence of tasks while continuously exploring the environment
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
- Trace2Tower: Transition-Aware EigenTrace Induction of Multi-Level Skills for LLM Agents#972197
- ArenaFlow: From Trajectory Ranking to Hierarchical Credit Propagation for Open-Ended Agent RL#1035782
- Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems#1118475
- Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms#1035607
- Simulate to Generalize: Scaling Stateful Supervision for API-calling Agents using LLM World Models#1052239
Record · ID 1048709
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