SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment

Researchers introduce SkillC, a reinforcement learning framework that enables LLM agents to internalize external skills during training rather than relying on them at runtime. The method uses contrastive credit assignment to distinguish skill-dependent from autonomous success, achieving 4.4-5.5% performance improvements over prior internalization approaches on complex tasks.

SkillC addresses a fundamental challenge in autonomous agent development: how to transition from skill-assisted learning to independent operation. Traditional skill-augmented RL methods keep external skills available during inference, creating dependency; internalization methods attempt withdrawal but lack mechanisms to properly credit autonomous versus assisted successes. This research solves that problem through contrastive learning, sampling paired scenarios where tasks are completed with and without skill assistance within single policy updates.

The framework's innovation lies in its dual-stream advantage estimator, which preserves global reward ranking while applying one-sided corrections favoring skill-free success. This design prevents catastrophic performance drops during the transition from assisted to autonomous operation. The adaptive curriculum layer further optimizes this internalization process by dynamically adjusting attribution strength and actively pruning less valuable skills.

For the AI agent development community, SkillC represents progress toward more capable and self-sufficient autonomous systems. Strong performance on ALFWorld and WebShop—complex environments requiring web navigation and household task reasoning—demonstrates practical applicability beyond toy problems. The 4.4-5.5% improvement margins suggest meaningful advances in agent competence.

The research validates that explicit contrastive signals outperform implicit curriculum approaches. As autonomous agents increasingly handle real-world tasks, eliminating runtime skill dependencies becomes critical for deployment reliability and cost efficiency. Future work likely extends these techniques to multi-agent scenarios and broader skill domains. The methodology establishes a template for skill transfer that other RL frameworks may adopt.

• →SkillC uses contrastive credit assignment to distinguish skill-dependent from autonomous success in agent learning
• →The framework achieves 4.4-5.5% performance gains over prior skill-internalization baselines without runtime skill access
• →Dual-stream advantage estimators enable smooth transitions from skill-assisted to independent agent operation
• →Adaptive curriculum mechanisms dynamically optimize skill attribution strength and active skill set composition
• →Strong experimental results on complex environments (ALFWorld, WebShop) indicate practical deployment potential