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

2026-06-18 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

The "Connect the Dots" (CoD) framework introduces a general approach for training large language models (LLMs) to function as long-lifecycle agents. This framework enables LLMs to continuously explore environments, learn from their own experiences, and iteratively self-update their context, thereby achieving progressively better performance on future tasks. Major components include an end-to-end reinforcement learning (RL) algorithm, specifically a GRPO-style method with fine-grained credit assignment, designed for long rollout sequences that interleave task-solving and context-updating episodes. The framework also provides tailored tasks and environments to incentivize and measure this meta-capability. Empirical results validate the efficacy of this end-to-end RL training and demonstrate its potential for out-of-distribution generalization within and across different domains.

Key takeaway

For AI Scientists and Machine Learning Engineers developing autonomous agents, this research highlights the importance of training LLMs for long-lifecycle capabilities. You should consider integrating end-to-end reinforcement learning with continuous context updating to achieve robust cross-domain generalization. This approach can significantly enhance agent performance and adaptability in complex, evolving environments, moving beyond single-task learning and improving long-term utility.

Key insights

Training LLMs with end-to-end RL for long-lifecycle agents fosters continuous learning and cross-domain generalization.

Principles

• Agents require continuous context self-updating
• Meta-capabilities drive cross-domain generalization

Method

Employs end-to-end reinforcement learning with long rollout sequences, interleaving task-solving and context-updating episodes, using a GRPO-style algorithm with fine-grained credit assignment.

In practice

• Implementations are publicly released on GitHub
• Demonstrates out-of-distribution generalization

Topics

• Large Language Models
• Reinforcement Learning
• AI Agents
• Cross-Domain Generalization
• Long-Lifecycle Agents
• GRPO Algorithm

Code references

• agentscope-ai/Trinity-RFT

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.