SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment
arXiv:2605.27899v1 Announce Type: new Abstract: Structured skill prompts improve exploration in long-horizon agentic reinforcement learning (RL). Skill-augmented RL methods retain external skills at inference, while skill-internalization RL methods withdraw them during training to enable autonomous performance. However, existing internalization approaches only use skill-helpfulness contrast for curriculum control, leaving the policy update unchanged and unable to distinguish skill-dependent from autonomous success. We propose SkillC, a framework based on Contrastive Skill Credit Assignment (CS
The rapid advancement in LLMs and reinforcement learning is naturally leading to research focused on autonomous agents and their ability to learn complex tasks. Techniques like SkillC are a logical next step to enable more sophisticated agentic behavior.
This research is critical for developing more capable and general-purpose AI agents that can operate effectively in novel environments without constant external guidance. It represents a move towards truly autonomous AI systems.
Existing approaches to skill internalization in LLM agents often fail to distinguish between skill-dependent and autonomous success; SkillC introduces contrastive credit assignment to address this limitation, allowing agents to better internalize skills themselves.
- · AI developers
- · Robotics
- · Automation companies
- · Manual low-skill labor
- · Inefficient SaaS providers
LLM agents will become more adept at autonomous skill learning and adaptation in complex environments.
This improved autonomy could accelerate the deployment of AI agents in various white-collar and specialized tasks, collapsing existing workflows.
The enhanced capabilities of autonomous agents might lead to further discussions on AI ethics, control, and the societal impact of increasingly self-sufficient AI systems.
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Read at arXiv cs.AI