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Scheduling Recursive Reasoning in Looped Transformers
Boyuan Wang, Chengyao Yu, Jiaxi Ren +3
Recurrent reasoning models have attracted growing attention for scaling test-time computation, typically by iteratively refining latent states with shared parameters. However, thes…
On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models
Shuoyuan Wang, Beier Luo, Hao Zeng +5
Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a promising way to optimize ca…
Anytime-Valid LLM Leaderboards via Benchmark-weighted and Block-Factorized e-Processes
Hongfu Gao, Songxin Zhang, Zejian Xie +3
Large language model (LLM) leaderboards compare model capabilities by ranking models according to their mean performance on fixed benchmarks. However, variability in evaluation out…
HSAP: A Hierarchical Sequence-aware Parallelism for Hybrid-Context Generative Models
Songxin Zhang, Zejian Xie, Zhuoyang Song +4
In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly…
RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
Sai Hao, Hao Zeng, Hongxin Wei +1
Efficiently routing queries to the optimal large language model (LLM) is crucial for optimizing the cost-performance trade-off in multi-model systems. However, most existing router…
HyPAC: Cost-Efficient LLMs-Human Hybrid Annotation with PAC Error Guarantees
Hao Zeng, Huipeng Huang, Xinhao Qu +3
Data annotation often involves multiple sources with different cost-quality trade-offs, such as fast large language models (LLMs), slow reasoning models, and human experts. In this…
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
Xuanning Zhou, Zihao Shi, Hao Zeng +3
Conformal prediction (CP) is a powerful framework for uncertainty quantification, generating prediction sets with coverage guarantees. Split conformal prediction relies on labeled…
Parametric Scaling Law of Tuning Bias in Conformal Prediction
Hao Zeng, Kangdao Liu, Bingyi Jing +1
Conformal prediction is a popular framework of uncertainty quantification that constructs prediction sets with coverage guarantees. To uphold the exchangeability assumption, many c…
TorchCP: A Python Library for Conformal Prediction
Jianguo Huang, Jianqing Song, Xuanning Zhou +2
Conformal prediction (CP) is a powerful statistical framework that generates prediction intervals or sets with guaranteed coverage probability. While CP algorithms have evolved bey…
Exploring Learning Complexity for Efficient Downstream Dataset Pruning
Wenyu Jiang, Zhenlong Liu, Zejian Xie +3
The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task per…