ROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition

• URL: http://arxiv.org/abs/2601.03822v1
• Date: Wed, 07 Jan 2026 11:30:55 GMT
• Title: ROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition
• Abstract summary: We study budgeted inference-time reasoning for multiple tasks under a strict global token constraint.<n>This perspective highlights a meta-cognitive requirement -- anticipating task difficulty, estimating return over investment.<n>We propose ROI-Reasoning, a two-stage framework that endows LLMs with intrinsic, budget-aware rationality.
• Score: 11.094392304740134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) can achieve strong reasoning performance with sufficient computation, but they do not inherently know how much computation a task requires. We study budgeted inference-time reasoning for multiple tasks under a strict global token constraint and formalize it as a Ordered Stochastic Multiple-Choice Knapsack Problem(OS-MCKP). This perspective highlights a meta-cognitive requirement -- anticipating task difficulty, estimating return over investment (ROI), and allocating computation strategically. We propose ROI-Reasoning, a two-stage framework that endows LLMs with intrinsic, budget-aware rationality. In the first stage, Meta-Cognitive Fine-Tuning teaches models to predict reasoning cost and expected utility before generation, enabling explicit solve-or-skip decisions. Next, Rationality-Aware Reinforcement Learning optimizes sequential decision making under a hard token budget, allowing models to learn long-horizon allocation strategies. Across budgeted mathematical reasoning benchmarks, ROI-Reasoning consistently improves overall score while substantially reducing regret under tight computation budgets.

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