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Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

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 natural alternative is to let the agent emit variable-length action chunks. However, naively training such policies with standard reinforcement learning fails: the agent either collapses to single-action behavior or over-commits to excessively long sequences. Both failures share a common root cause: the i

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