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

2026-06-19 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The "Connect the Dots" (CoD) framework introduces a method for training large language models (LLMs) to act as long-lifecycle agents capable of cross-domain generalization through end-to-end reinforcement learning (RL). This framework enables LLMs to continuously solve task sequences, explore environments, learn from experiences, and iteratively self-update their context for improved future performance. Key components include an RL algorithm and infrastructure supporting long rollout sequences that interleave task-solving and context-updating episodes, alongside specialized tasks and environments. Proof-of-concept implementations, utilizing a GRPO-style RL algorithm with fine-grained credit assignment, demonstrate the framework's efficacy. For instance, training Qwen3-8B-Instruct on FrozenLake-Obscure environments increased the success rate for a fourth task, conditioned on self-updated context, from 28% to 76%, validating significant out-of-distribution generalization across domains and to Ralph-loop settings. Implementations are publicly available.

Key takeaway

For AI Engineers developing long-lifecycle LLM agents, recognize that standard task-by-task reinforcement learning is insufficient for continuous self-improvement. You should consider adopting the "Connect the Dots" (CoD) framework to explicitly train LLMs for adaptive context management and cross-domain generalization. This approach, which interleaves task-solving with context-updating episodes, significantly enhances an agent's ability to learn and perform in underspecified, dynamic environments, moving beyond human-crafted scaffolds.

Key insights

LLMs can achieve continuous self-improvement and cross-domain generalization by learning to self-update context via end-to-end RL.

Principles

• Long-lifecycle agents need continuous context self-updating.
• End-to-end RL elicits LLM meta-capabilities.
• Credit assignment must span task and context episodes.

Method

The CoD-Train method employs a GRPO-style RL algorithm with fine-grained credit assignment, calculating episode returns as the mean reward of current and future solve-task episodes.

In practice

• Train LLMs for adaptive context management.
• Design environments incentivizing context transfer.
• Apply to personal assistants or coding agents.

Topics

• LLM Training
• Reinforcement Learning
• AI Agents
• Context Management
• Cross-Domain Generalization
• Lifelong Learning

Code references

• agentscope-ai/Trinity-RFT
• agentscope-ai/QwenPaw
• anthropics/claude-code
• nousresearch/hermes-agent
• openclaw/openclaw

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.