arXiv Machine Learning June 5, 2026 By Sora Miyamoto, Daisuke Oba, Naoaki Okazaki

Aligning Tree-Search Policies with Fixed Token Budgets in Test-Time Scaling of LLMs

Read the original on arXiv Machine Learning →

arXiv:2602. 09574v2 Announce Type: replace-cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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