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

Sunny, Qi Qi, Hao Sun

ROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition: 5 upvotes on Hugging Face Daily Papers, #5 of 20 papers on 2026-01-08. Day-by-day upvote history. It lost 20 votes when the Hub removed votes in bulk.

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.

Paper page on Hugging Face · arXiv

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