Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning

• URL: http://arxiv.org/abs/2605.11625v1
• Date: Tue, 12 May 2026 06:51:31 GMT
• Title: Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning
• Abstract summary: Large reasoning models (LRMs) improve problem solving through extended reasoning, but often misallocate test-time compute.<n>We propose Budget-Efficient Thinking (BET), a two-stage framework that combines behavioral cold-start with GRPO under an investment-cost-aware reward.<n>BET reduces reasoning tokens by 55% on average while achieving overall performance improvements.
• Score: 9.687158786222994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large reasoning models (LRMs) improve problem solving through extended reasoning, but often misallocate test-time compute. Existing efficiency methods reduce cost by compressing reasoning traces or conditioning budget on perceived difficulty, yet largely overlook solvability. As a result, they may spend large budgets on queries beyond the model's capability while compressing hard-but-solvable queries that require deeper reasoning. In this work, we formulate adaptive reasoning as a computational investment under uncertainty, where budget should follow the expected return of reasoning rather than perceived difficulty alone. To instantiate this principle, we propose Budget-Efficient Thinking (BET), a two-stage framework that combines behavioral cold-start with GRPO under an investment-cost-aware reward. By aligning solve-or-fold decisions with rollout-derived solvability, BET learns three behaviors: (1) short solve, answering easy queries concisely; (2) nice fold, abstaining early when continued reasoning has near-zero expected return; and (3) hero call, preserving sufficient compute for hard-but-solvable queries. Across seven benchmarks and three base models, BET reduces reasoning tokens by ~55% on average while achieving overall performance improvements, and transfers zero-shot from mathematical reasoning to scientific QA and logical reasoning with comparable efficiency gains.

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