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About this paper
Qingyao Li, Wenxiang Jiao, Shuai Shao, Kangning Zhang, Yuan Lu, Yi Guo, Weiwen Liu, Weinan Zhang and 1 more
SkillGate introduces a method to train in-policy skill selection for LLM agents by partitioning credit assignment into disjoint channels for task execution and skill selection. This approach mitigates "selector credit starvation," leading to a substantial improvement in task success rates and more accurate, selective skill utilization in long-horizon tasks.
Key insights
- A fundamental "selector credit starvation" problem impairs LLM agent learning in long-horizon tasks, where a global outcome reward fails to provide an effective signal for critical skill selection decisions.
- Optimal learning for skill selection requires a credit signal distinct from task execution, as the evidence for judging each decision type is fundamentally different.
- Targeted, disentangled credit assignment is more effective for improving in-policy skill selection than relying on increased model scale or external skill routing mechanisms.
Summary by alphaXiv.
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