Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917277301997568 |
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| author | Paglieri, Davide Cupiał, Bartłomiej Cook, Jonathan Piterbarg, Ulyana Tuyls, Jens Grefenstette, Edward Foerster, Jakob Nicolaus Parker-Holder, Jack Rocktäschel, Tim |
| author_facet | Paglieri, Davide Cupiał, Bartłomiej Cook, Jonathan Piterbarg, Ulyana Tuyls, Jens Grefenstette, Edward Foerster, Jakob Nicolaus Parker-Holder, Jack Rocktäschel, Tim |
| contents | 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 conceptual framework formalizing dynamic planning for LLM agents, enabling them to flexibly decide when to allocate test-time compute for planning. We propose a simple two-stage training pipeline: (1) supervised fine-tuning on diverse synthetic data to prime models for dynamic planning, and (2) RL to refine this capability in long-horizon environments. Experiments on the Crafter environment show that dynamic planning agents trained with this approach are more sample-efficient and consistently achieve more complex objectives. Additionally, we demonstrate that these agents can be effectively steered by human-written plans, surpassing their independent capabilities and highlighting the potential for safer and more collaborative agentic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents Paglieri, Davide Cupiał, Bartłomiej Cook, Jonathan Piterbarg, Ulyana Tuyls, Jens Grefenstette, Edward Foerster, Jakob Nicolaus Parker-Holder, Jack Rocktäschel, Tim Artificial Intelligence 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 conceptual framework formalizing dynamic planning for LLM agents, enabling them to flexibly decide when to allocate test-time compute for planning. We propose a simple two-stage training pipeline: (1) supervised fine-tuning on diverse synthetic data to prime models for dynamic planning, and (2) RL to refine this capability in long-horizon environments. Experiments on the Crafter environment show that dynamic planning agents trained with this approach are more sample-efficient and consistently achieve more complex objectives. Additionally, we demonstrate that these agents can be effectively steered by human-written plans, surpassing their independent capabilities and highlighting the potential for safer and more collaborative agentic systems. |
| title | Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03581 |