Hugging Face Trending Papers
RL-ARC: Calibrating Large Reasoning Models via Reasoning-guided Uncertainty
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arXiv AI
5d ago
RL-ARC: Calibrating Large Reasoning Models via Reasoning-guided Uncertainty
RL-ARC is a calibration‑aware training framework that jointly uses reasoning confidence and answer confidence to improve the calibration of large reasoning models. It applies reasoning‑guided regularization for correct predictions and an overconfidence penalty for incorrect ones. Experiments on in‑distribution and out‑of‑distribution data show that RL‑ARC enhances calibration while preserving reasoning performance.
arXiv Machine Learning
Aug 11
Process Supervision of Confidence Margin for Calibrated LLM Reasoning
arXiv:2604. 23333v2 Announce Type: replace Abstract: Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability.
arXiv Machine Learning
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Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains
arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm,...
arXiv AI
Oct 5
Test-time Calibration Learning for Large Language Model Reasoning
The paper introduces Test-Time Calibration Learning (TTCL), a label‑free framework that adapts both reasoning accuracy and verbalized confidence of large language models directly on unlabeled target‑task data. TTCL generates self‑supervision signals from multiple model responses, enabling calibration without ground‑truth labels and proving theoretically as a bounded surrogate for the ideal calibration objective. Experiments on mathematical reasoning and factual question answering show consistent improvements, with base models gaining an average 40.13% in accuracy and 70.80% in ECE reduction across eight benchmarks, and further gains under domain shift.
arXiv AI
Jun 4
Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots
arXiv:2606. 04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset.
arXiv Machine Learning
Jul 14
The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models
arXiv:2603. 16728v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly deployed in high-stakes settings where reliable uncertainty quantification (UQ) is as important as predictive accuracy.