arXiv AI
ROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition
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arXiv AI
Aug 11
Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions
arXiv:2608. 07968v1 Announce Type: cross Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one question at a time.
arXiv Machine Learning
Oct 8
Continuous-Utility Direct Preference Optimization
Continuous-Utility Direct Preference Optimization (CU‑DPO) replaces binary preference labels with continuous scores to better capture fine‑grained reasoning quality. The framework aligns language models to multiple prompt‑based cognitive strategies, proving a θ(K log K) sample‑complexity improvement and convergence to an entropy‑regularized utility‑maximizing policy. A two‑stage pipeline—strategy selection via best‑vs‑all comparisons and execution refinement using margin‑stratified pairs—yields significant gains on mathematical reasoning, code generation, and causal reasoning benchmarks.
arXiv AI
Aug 11
Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents
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.
arXiv AI
Sep 28
Nice Fold or Hero Call: Learning Budget-Efficient Thinking under Policy-Dependent Solvability
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.
arXiv AI
5d ago
e1: Learning Adaptive Control of Reasoning Effort
arXiv:2510.27042v3 Announce Type: replace Abstract: Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Users m...
arXiv AI
Jun 17
SuCo: Sufficiency-guided Continuous Adaptive Reasoning
arXiv:2606. 17687v1 Announce Type: cross Abstract: Despite remarkable performance on complex tasks, Large Reasoning Models (LRMs) often generate excessively long Chain-of-Thoughts (CoT), inflating computational costs even for simple queries.