arXiv AI

Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning

The paper introduces TASCO, a test‑time adaptation framework that enhances Large Language Model reasoning by incorporating local stability into confidence‑based adaptation while keeping the model frozen. TASCO optimizes a lightweight task‑level prefix using two perturbation strategies—Random Perturbation for distributional stability and Sharpness‑Aware Perturbation for worst‑case sensitivity—to ensure that high confidence aligns with correctness. Experiments show that TASCO improves reasoning accuracy and token efficiency across various LLMs and benchmarks, and behavioral analyses confirm that it maintains stable confidence without over‑concentrating the predictive distribution.

Hugging Face Trending Papers

Sep 10

Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning

The paper introduces TASCO, a test‑time adaptation method that improves Large Language Model reasoning by optimizing stability‑aware confidence. It keeps the LLM frozen and uses a lightweight task‑level prefix, applying Random Perturbation for distributional stability and Sharpness‑Aware Perturbation for worst‑case sensitivity. Experiments show enhanced reasoning accuracy and token efficiency across various LLMs and benchmarks, while preserving stable confidence without over‑concentrating predictions.

llms benchmarks

arXiv AI

Jun 16

Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention

arXiv:2510. 13940v4 Announce Type: replace-cross Abstract: Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency.

By Zhen Yang, Mingyang Zhang, Feng Chen, Ganggui Ding, Liang Hou, Xin Tao, Ying-Cong Chen

llms efficiency benchmarks

arXiv AI

Jul 9

Future Confidence Distillation in Large Language Models

arXiv:2607. 07626v1 Announce Type: cross Abstract: Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as retrieval, tool use, and adaptive computation depend on accurately estimating answer reliability.

By Sahil Kale

llms agents efficiency

arXiv AI

Jun 3

Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification

arXiv:2606. 03608v1 Announce Type: cross Abstract: Test-time reinforcement learning has emerged as a promising paradigm for enhancing the complex reasoning abilities of large language models in a completely label-free manner.

By Jiahui Li, Jianfeng Shan, Wenpei Chen, Shunyu Wu, Jian Lou, Wenjie Feng, Dan Li, See-Kiong Ng

llms reinforcement-learning benchmarks

arXiv AI

Sep 16

Post-Training Large Language Models via Reinforcement Learning from Self-Feedback

arXiv:2507.21931v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often produce plausible but poorly-calibrated answers, limiting their reliability on reasoning-intensive tasks....

By Carel van Niekerk, Renato Vukovic, Benjamin Ruppik, Hsien-chin Lin, Shutong Feng, Milica Ga\v{s}i\'c

llms nlp reinforcement-learning fine-tuning

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.

By Liaoyaqi Wang, Chunsheng Zuo, William Jurayj, Benjamin Van Durme, Anqi Liu

llms reinforcement-learning benchmarks safety

arXiv Computation and Language

1d ago

Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning

arXiv:2604.08974v2 Announce Type: replace Abstract: Uncertainty quantification techniques measure confidence in language model outputs to support critical applications like hallucination detection an...

By Lorenzo Jaime Yu Flores, Cesare Spinoso di-Piano, Jackie Chi Kit Cheung

llms nlp fine-tuning safety

arXiv Computation and Language

Sep 15

Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering

arXiv:2601.19847v3 Announce Type: replace Abstract: Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often require...

By Fangan Dong, Zuming Yan, Xuri Ge, Zhiwei Xu, Mengqi Zhang, Xuanang Chen, Ben He, Xin Xin, Zhumin Chen, Ying Zhou

llms benchmarks

arXiv AI

Aug 11

Understanding and Mitigating Premature Confidence for Better LLM Reasoning

arXiv:2605. 24396v2 Announce Type: replace Abstract: Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps, limiting the gains from additional test-time compute.

By Jingchu Gai, Guanning Zeng, Christina Baek, Chen Wu, J. Zico Kolter, Andrej Risteski, Aditi Raghunathan

llms reinforcement-learning benchmarks safety

arXiv AI

Jul 28

Multi-Task GRPO: Reliable LLM Reasoning Across Tasks

arXiv:2602. 05547v2 Announce Type: replace-cross Abstract: RL-based post-training with GRPO is widely used to improve large language models on individual reasoning tasks.

By Shyam Sundhar Ramesh, Xiaotong Ji, Matthieu Zimmer, Sangwoong Yoon, Zhiyong Wang, Haitham Bou Ammar, Aurelien Lucchi, Ilija Bogunovic

llms reinforcement-learning

arXiv Machine Learning

Jul 9

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

arXiv:2607. 06974v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in isolation.

By Ruilin Tong, Dong Gong

llms rag

arXiv Machine Learning

Aug 11

Consilience for Verifier-Free Test-Time Scaling

arXiv:2608. 09898v1 Announce Type: cross Abstract: Test-time scaling often uses an external verifier, such as compilers and test cases in coding or trained value functions in robotics applications, to obtain high-quality rollouts.

By Lecheng Kong, Like Hui, Haitao Mao, Jun Huan

llms robotics

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