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Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents

arXiv:2603.12634v1 Announce Type: new Abstract: Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end trajectories. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-execution. We propose the Budget-Aware Value Tree (BAVT), a training-free inference-time framework that models multi-hop reasoning as a dynamic search tree guided by step-level value estimation within a single LLM backbone. Another key innovation is a budget-conditioned node selection mechanism that uses the remaining resource ratio as a natural scaling exponent over node values, providing a principled, parameter-free transition from broad exploration to greedy exploitation as the budget depletes. To combat the well-known overconfidence of LLM self-evaluation, B

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Yushu Li, Wenlong Deng, Jiajin Li, Xiaoxiao Li · · 1 min read · 8 views
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arXiv:2603.12634v1 Announce Type: new Abstract: Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end trajectories. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-execution. We propose the Budget-Aware Value Tree (BAVT), a training-free inference-time framework that models multi-hop reasoning as a dynamic search tree guided by step-level value estimation within a single LLM backbone. Another key innovation is a budget-conditioned node selection mechanism that uses the remaining resource ratio as a natural scaling exponent over node values, providing a principled, parameter-free transition from broad exploration to greedy exploitation as the budget depletes. To combat the well-known overconfidence of LLM self-evaluation, BAVT employs a residual value predictor that scores relative progress rather than absolute state quality, enabling reliable pruning of uninformative or redundant tool calls. We further provide a theoretical convergence guarantee, proving that BAVT reaches a terminal answer with probability at least $1-\epsilon$ under an explicit finite budget bound. Extensive evaluations on four multi-hop QA benchmarks across two model families demonstrate that BAVT consistently outperforms parallel sampling baselines. Most notably, BAVT under strict low-budget constraints surpasses baseline performance at $4\times$ the resource allocation, establishing that intelligent budget management fundamentally outperforms brute-force compute scaling.

Executive Summary

The article 'Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents' proposes the Budget-Aware Value Tree (BAVT), a novel inference-time framework for Large Language Model (LLM) agents. BAVT addresses the issue of test-time scaling by introducing a dynamic search tree guided by step-level value estimation and a budget-conditioned node selection mechanism. This approach enables reliable pruning of uninformative or redundant tool calls and provides a principled transition from exploration to exploitation as the budget depletes. The authors demonstrate that BAVT consistently outperforms parallel sampling baselines across four multi-hop QA benchmarks, even under strict low-budget constraints. This breakthrough has significant implications for the future of LLM agents and efficient reasoning.

Key Points

  • ▸ BAVT is a training-free inference-time framework for LLM agents.
  • ▸ The framework introduces a dynamic search tree guided by step-level value estimation.
  • ▸ A budget-conditioned node selection mechanism enables principled transition from exploration to exploitation.

Merits

Strength in addressing the test-time scaling paradigm

BAVT provides a novel solution to the test-time scaling problem, addressing the issue of compute as an abundant resource.

Improvement over existing budget-aware methods

BAVT outperforms existing budget-aware methods that require expensive fine-tuning or coarse, trajectory-level heuristics.

Demerits

Limited evaluation on diverse LLM architectures

The article primarily evaluates BAVT on two model families, and further evaluation on diverse LLM architectures would provide a more comprehensive understanding of its effectiveness.

Expert Commentary

The article presents a compelling case for the Budget-Aware Value Tree (BAVT) as a novel and effective solution to the test-time scaling problem in LLM agents. The framework's ability to provide a principled transition from exploration to exploitation and its performance under strict low-budget constraints are particularly noteworthy. However, further evaluation on diverse LLM architectures and exploration of potential applications in real-world scenarios would provide a more comprehensive understanding of BAVT's potential. Nonetheless, the article's contributions to the field of LLM agents and efficient reasoning are significant and warrant further investigation.

Recommendations

  • ✓ Future research should focus on evaluating BAVT on diverse LLM architectures and exploring its applications in real-world scenarios.
  • ✓ The development of BAVT highlights the need for more efficient and cost-effective LLM agents, and researchers should continue to explore innovative solutions to address this challenge.

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