Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
#LLM agents #test-time compute #ReAct prompting #reinforcement learning #planning allocation #long‑horizon tasks
📌 Key Takeaways
- Reinforcement learning improves LLM problem‑solving but standard ReAct prompting is computationally expensive.
- Constant planning degrades long‑horizon task performance; no planning hampers capability.
- The study introduces a selective planning strategy to balance compute cost and effectiveness.
- The approach is tested on extended tasks, showing reduced compute usage without loss of accuracy.
- Results suggest efficient test‑time compute allocation is crucial for practical LLM agent deployment.
📖 Full Retelling
Researchers on the arXiv preprint "Learning When to Plan: Efficiently Allocating Test‑Time Compute for LLM Agents" (arXiv:2509.03581v3) have proposed a new strategy for large language model (LLM) agents. The paper investigates how reinforcement learning enhances LLM reasoning capabilities, but finds that prompting an LLM to explicitly plan before every action leads to high computational cost and degrades performance on long‑horizon tasks, while never planning limits problem‑solving ability. To address this trade‑off, the authors introduce a method that selectively determines when an agent should plan prior to acting, aiming to reduce compute usage while maintaining or improving performance on extended tasks.
🏷️ Themes
Large Language Models, Reinforcement Learning, Agentic Reasoning, Compute Efficiency, Planning Strategies
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Original Source
arXiv:2509.03581v3 Announce Type: replace
Abstract: Training large language models (LLMs) to reason via reinforcement learning (RL) significantly improves their problem-solving capabilities. In agentic settings, existing methods like ReAct prompt LLMs to explicitly plan before every action; however, we demonstrate that always planning is computationally expensive and degrades performance on long-horizon tasks, while never planning further limits performance. To address this, we introduce a conc
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