SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training
arXiv:2606.02355v1 Announce Type: cross Abstract: Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or persistent skill retrieval at inference, increasing engineering complexity, context length, and deployment latency. We propose Self-Internalizing Reinforcement learning with Intrinsic skills (SIRI), a three-phase framework that enables agents to discover, validate, and internalize skills without external skill generators or inference-time skill banks. SIRI first warms up the policy with GiGPO to a
The rapid advancement in large language models has exposed limitations in long-horizon task execution, driving immediate research focus on more autonomous and efficient agent architectures.
This breakthrough offers a path to more capable and less resource-intensive LLM agents, potentially accelerating the deployment and impact of AI across various industries.
LLM agents can now discover, validate, and internalize reusable skills without constant external intervention, reducing computational overhead and engineering complexity.
- · AI software developers
- · Enterprises adopting AI agents
- · Researchers in reinforcement learning
- · Cloud computing providers
- · Consulting firms reliant on manual process optimization
- · Legacy automation software vendors
More robust and autonomous AI agents become feasible for complex, multi-step tasks.
Reduced operational costs and increased efficiency in white-collar workflows due to self-improving AI agents.
Accelerated development of general-purpose AI systems as agents can learn and adapt skills intrinsically.
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Read at arXiv cs.LG