Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
TL;DR
Agentic reinforcement learning (RL) struggles to create reusable strategies that generalize across tasks. ReSkill is introduced as a method that integrates skill creation with policy optimization in RL.
✦ Why It Matters
Engineers can enhance RL agent performance by integrating skill creation with policy optimization using ReSkill.
Key Takeaways
- Integrate skill creation with policy optimization in RL.
- Utilize assertion-driven skill revisions for better performance.
- Employ controlled rollout sampling for effective skill comparison.
- Adopt Thompson Sampling to balance exploration and exploitation.
How It Works
ReSkill operates by embedding three key mechanisms within the GRPO framework. First, it uses an assertion-driven skill creator that analyzes past experiences to identify failures and propose conditional skill revisions.
Second, it implements within-group rollout sampling, allowing for a controlled comparison of different skill versions to determine which best supports the evolving policy. Lastly, it employs Thompson Sampling with adaptive discounting to effectively balance exploration of new skills and exploitation of existing ones as the policy matures.