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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
- Explore the use of SKILLC for autonomous skill learning in RL.
- Implement dual-stream advantage estimators to optimize learning signals.
- Utilize adaptive curricula to refine skill attribution in training.
- Test SKILLC in your own RL environments for performance improvements.
How It Works
SKILLC leverages Contrastive Skill Credit Assignment (CSCA) by sampling two types of rollouts: one that incorporates external skills and one that does not. This contrast allows the agent to learn from the differences in task performance, effectively creating a direct learning signal for internalization.
The dual-stream advantage estimator maintains a global ranking of performance while applying corrections to emphasize skill-free successes, thus enhancing the agent's ability to internalize skills autonomously.