Write, Execute, Refine: From Skill Followers to Skill Optimizers via Reinforcement Learning from Execution Feedback
Researchers have developed a framework called WER (Write, Execute, and Refine) that uses reinforcement learning to optimize natural language skills for tool-using agents. The framework consists of three phases: writing new skills, executing them repeatedly, and refining the skills based on their outcomes. This approach improves performance by 7-11 points compared to using no skill at all. The trained optimizer outperforms off-the-shelf models in some cases, reaching a high ac
Researchers have developed a framework called WER (Write, Execute, and Refine) that uses reinforcement learning to optimize natural language skills for tool-using agents. The framework consists of three phases: writing new skills, executing them repeatedly, and refining the skills based on their outcomes. This approach improves performance by 7-11 points compared to using no skill at all. The trained optimizer outperforms off-the-shelf models in some cases, reaching a high accuracy of 76.63 percent.
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Why it matters: This work matters because it addresses a significant gap in AI research: the ability to optimize skills for tool-using agents. By developing a framework that can learn from execution feedback, researchers can improve the performance of these agents and potentially apply this approach to other areas of AI.
Source: https://arxiv.org/abs/2608.17587
This article was originally published at: https://arxiv.org/abs/2608.17587