Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning

Researchers present the 'Connect the Dots' (CoD) framework for training large language models to function as long-lifecycle agents that learn from experience and progressively improve performance across tasks. The work combines reinforcement learning with self-updating context mechanisms, demonstrating cross-domain generalization capabilities and releasing implementations to advance AI agent research.

This research addresses a fundamental challenge in AI development: enabling language models to operate effectively as autonomous agents over extended periods while learning from their own experiences. The CoD framework represents a methodological advance in how LLMs can be trained to maintain and update their understanding of environments, moving beyond single-task performance toward meta-learning capabilities that improve with exposure.

The work builds on established reinforcement learning principles but applies them specifically to the long-horizon agent problem, where traditional task-by-task training proves insufficient. By implementing fine-grained credit assignment within a GRPO-style algorithm and designing evaluation environments that specifically measure the ability to connect contextual dots, the researchers create infrastructure aligned with real-world agent deployment scenarios.

The significance lies in demonstrated out-of-distribution generalization—the framework shows promise across multiple domains and transfer to different deployment patterns. This capability directly addresses deployment challenges for autonomous AI systems, where environments inevitably contain novel situations not present during training.

The release of code through AgentScope positions this as a research foundation rather than isolated academic contribution. For the AI development ecosystem, this work signals progress toward more resilient, adaptable autonomous systems. The emphasis on cross-domain generalization particularly matters for practical deployment, where agents must handle environmental variation and novel task combinations. Future development hinges on scaling these approaches and validating performance in real-world settings beyond controlled research environments.

• →CoD framework enables LLMs to learn from sequential task experience and update internal context for improved future performance.
• →End-to-end reinforcement learning with long rollout sequences demonstrates efficacy for training meta-capabilities in language models.
• →Framework achieves out-of-distribution generalization within domains, across domains, and between different deployment settings.
• →Released implementations through AgentScope facilitate reproducibility and accelerate downstream research in AI agent development.
• →Research bridges theoretical advances in LLM training with practical infrastructure requirements for long-lifecycle autonomous agents.