SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection
Agentic reinforcement learning (RL) enables large language models (LLMs) to learn from multi-turn interactions with verifiable outcomes.
ProofPaper ↗
Key points
- Textual skills can improve exploration by distilling prior interactions into reusable guidance.
- To address these challenges, we introduce SkillScaffold, a policy-adaptive training scaffold that combines low-cost skill-utility estimation with selective skill injection.
- Scaffolded RL encourages skill internalization for skill-free inference.
- Experiments on ALFWorld, WebShop, ScienceWorld, and $τ^2$-Bench demonstrate gains over strong agentic-RL and skill-based RL baselines across both short- and long-horizon tasks.
Sources (1)
- [1]SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective InjectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:46 PM
Agentic reinforcement learning (RL) enables large language models (LLMs) to learn from multi-turn interactions with verifiable outcomes.
Textual skills can improve exploration by distilling prior interactions into reusable guidance.
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