arXiv:2602.09574v2 Announce Type: replace Abstract: Tree-search decoding is an effective form of test-time scaling for large language models (LLMs), but real-world deployment often imposes a fixed per-query token budget that varies across settings. Existing tree-search policies are largely budget-agnostic, treating the budget merely as a termination condition, thereby risking late-stage over-branching or premature termination. We propose Budget-Guided MCTS (BG-MCTS), a tree-search decoding algorithm that aligns its search policy with the remaining token budget: it starts with broad exploration
The proliferation of large language models (LLMs) and their integration into diverse applications creates an immediate need for efficient and budget-aware decoding strategies.
Optimizing LLM performance under fixed token budgets enhances their practical deployment in cost-sensitive and real-time environments, directly impacting scalability and commercial viability.
Decoding strategies for LLMs are evolving from budget-agnostic approaches to those explicitly incorporating token budget constraints, leading to more efficient and adaptable model outputs.
- · LLM developers
- · AI application platforms
- · Cloud computing providers
- · SaaS companies leveraging LLMs
- · Inefficient LLM architectures
- · Companies with high LLM inference costs
- · Fixed-budget AI service providers
More efficient and cost-effective deployment of advanced LLMs in real-world applications becomes feasible.
This efficiency could accelerate the adoption of LLMs in new domains where budget constraints were previously prohibitive.
Increased LLM efficiency might further decentralize AI development and application, as smaller entities can afford to leverage powerful models.
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