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

ArXi:2605.11625v1 Announce Type: new 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.