The paper introduces Uncertainty-Aware Budget Allocation (UAB), a concave integer optimization framework that reallocates a fixed sampling budget for language model reasoning based on uncertainty estimated from initial samples. In Phase‑1, each question receives a small number of generations, and the disagreement among these answers (vote entropy) signals difficulty. Phase‑2 uses a marginal‑greedy algorithm to allocate the remaining budget, focusing on questions with high initial disagreement, leading to a 2.3% average accuracy improvement over uniform allocation across five open‑weight models and five reasoning benchmarks, with up to 5.5% gains on individual benchmarks, especially in low‑resource settings.
By Manh Nguyen, Sunil Gupta, Hung Le
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.
By Sora Miyamoto, Daisuke Oba, Naoaki Okazaki
The paper introduces LB-MCTS, a trajectory-structured optimization framework that combines Monte Carlo Tree Search, Bayesian Optimization, and Large Language Models to address the CASH problem in AutoML. It uses a shared tree state for algorithm selection, hyperparameter refinement, and synergy between BO and LLM proposals, with a reliability-aware policy that shifts from LLM-driven to BO-driven suggestions as the surrogate model improves. Experiments on 104 AMLB datasets show that LB-MCTS consistently outperforms existing BO-based, LLM-based, and hybrid baselines.
By Beicheng Xu, Weitong Qian, Lingching Tung, Yupeng Lu, Bin Cui
The paper introduces ExTS, a tree‑search policy designed for budget‑constrained agentic search where evaluation and generation costs are high. ExTS treats expansion as a value‑of‑information decision, combining discriminative reward shaping, a stochastic virtual child, and quality‑conditioned branching to allocate budget more effectively. Experiments on prompt optimization, code generation, molecular structure elucidation, and agentic workflow optimization show ExTS matching or surpassing task‑specific baselines with an average gain of +5.5% using a single configuration, and the authors also present pilot‑run diagnostics to guide adaptation to different problem structures.
By Haoyang Fang, Bernie Wang
arXiv:2604. 05164v3 Announce Type: replace-cross Abstract: As LLM reasoning performance plateaus, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces even for simple queries.
By Neharika Jali, Anupam Nayak, Gauri Joshi
arXiv:2607. 11089v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting.
By Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias, Adam Jozefiak, Ciamac C. Moallemi
Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, these models often exhibit "computational overthinking," generating redundant reasoning steps that increase latency and cost without improving accuracy.
The paper proposes a new framework for collaboration between a small language model (SLM) and a large language model (LLM) that treats the interaction as an information acquisition problem under an API budget constraint. Instead of delegating reasoning tasks, the SLM remains the primary reasoner and selectively queries the LLM advisor with targeted questions, using a three-stage RLVR approach to decide when to call the advisor, how to phrase queries, and how to integrate the responses. Experiments on mathematical reasoning and coding tasks show that this strategy improves the performance–cost tradeoff compared to existing baselines and can transfer to other advisor model families without additional training.
By Yongjun Kim, Xiaoxiao Li, Jaeho Lee
arXiv:2607.28077v2 Announce Type: replace
Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models, but prompt groups with identica...
By Shuang Liang, Haoyang Zhou, Yifan Gong, Guowei Wang, Xiting Wang
arXiv:2601.03822v2 Announce Type: replace
Abstract: Large language models (LLMs) can achieve strong reasoning performance with sufficient computation, but they do not inherently know how much computa...
By Muyang Zhao, Chunhao Tian, Qi Qi, Hao Sun
The paper introduces Budget‑Efficient Thinking (BET), a two‑stage framework that treats adaptive reasoning as a computational investment, aligning solve‑or‑fold decisions with expected return rather than perceived difficulty. BET learns three distinct behaviors: concise short solves for easy queries, early abstention (nice fold) when further reasoning is unlikely to pay off, and allocating sufficient compute (hero call) for hard‑but‑solvable questions. Experiments on seven benchmarks with three base models show BET cuts reasoning tokens by 54% while boosting accuracy by up to 3.2%, and it transfers effectively to scientific QA and logical reasoning tasks.
By Zhaomeng Zhou, Lan Zhang, Junyang Wang, Mu Yuan, Songlin Liu, Tingzhao Li, Yiqing Hu, Yumeng Zhao
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models, but prompt groups with identical rollout rewards consume generation budget without effective learning signals. Pre-rollout prompt selection can reduce this waste by screening prompts before rollout generation.