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Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents
Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol, issuing one primitive action per LLM round. While this enables frequent replanning, it is inefficient for long-horizon tasks where many rounds are spent on routine action sequences. A natura
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