Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 28·8 min read
SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment
TL;DR
Large language model (LLM) agents struggle to learn and reuse skills efficiently because they cannot properly assign credit—determining which actions led to success. SKILLC introduces a contrastive credit assignment method that enables LLM agents to autonomously internalize and retain learned skills.
✦ Why It Matters
Engineers can build LLM agents that learn and reuse skills more efficiently, reducing training overhead and improving task performance.
Key Takeaways
- Implement contrastive credit assignment to identify which actions drive task success in LLM agents.
- Enable autonomous skill internalization so agents learn reusable patterns without explicit supervision.
- Use trajectory comparison (successful vs. unsuccessful) to isolate skill contributions effectively.
- Measure skill retention and transfer performance to validate credit assignment mechanisms.
Full Summary
LLM agents (AI systems using large language models to make decisions) face a fundamental challenge: when they succeed at a task, they cannot easily identify which specific skills or actions caused that success, making it hard to reuse those skills later. SKILLC addresses this by implementing contrastive credit assignment—a technique that compares successful and unsuccessful trajectories to isolate which skills were actually responsible for positive outcomes.
The method enables agents to autonomously internalize skills, meaning they learn to recognize and store useful behavioral patterns without explicit human instruction. The approach uses contrastive learning (comparing similar and dissimilar examples) to distinguish skill contributions.
Results demonstrated improved skill retention and transfer across tasks, with agents showing measurable gains in reusing previously learned capabilities compared to agents without this credit assignment mechanism.