Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI

Sep 30

Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents

arXiv:2609.36393v1 Announce Type: cross Abstract: Traditional reinforcement learning (RL) techniques focus on maximizing expected cumulative reward, where each action assumes to take a constant unit...

By Muhang Tian, Sherry Yang

llms agents reinforcement-learning

arXiv Machine Learning

Sep 30

Theory on Attention Dynamics for Out-of-Distribution In-Context Learning

arXiv:2609.36448v1 Announce Type: new Abstract: Transformers have demonstrated remarkable in-context learning (ICL) capabilities, enabling them to perform new tasks without additional fine-tuning. Ho...

By Junze Deng, Daouda Sow, Sen Lin, Yingbin Liang

llms fine-tuning

arXiv Machine Learning

Sep 30

Do LLMs Really Forget? Hidden-State Leakage in Model Unlearning and How to Fix it

arXiv:2609.36612v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) is typically evaluated at the output level, where a model appears to suppress sensitive or undesirable conte...

By Hadi Reisizadeh, Jiajun Ruan, Sijia Liu, Mingyi Hong

llms benchmarks safety

arXiv Machine Learning

Sep 30

What Makes Recurrence Effective in Looped Language Models?

arXiv:2609.36636v1 Announce Type: new Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding...

By Xinlin Zhuang, Siyuan Wang, Imran Razzak, Weiyang Liu

llms

arXiv AI

Sep 30

Inducing Process Supervision from Outcome-Only Reinforcement Learning

arXiv:2609.36641v1 Announce Type: cross Abstract: Process reward models (PRMs) have become a key component for LLMs, as their step-level feedback supports both post-training and test-time reasoning....

By Shengda Fan, Xin Cong, Zhong Zhang, Haotian Chen, Yankai Lin

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Sep 30

Scheduling Recursive Reasoning in Looped Transformers

arXiv:2609.36653v1 Announce Type: new Abstract: Recurrent reasoning models have attracted growing attention for scaling test-time computation, typically by iteratively refining latent states with sha...

By Boyuan Wang, Chengyao Yu, Jiaxi Ren, Hongxin Wei, Bingyi Jing, Yuxin Tao

llms

arXiv Machine Learning

Sep 30

Know Thyself, Teach Thyself: Internal Information Flow for Selective Self-Distillation

arXiv:2609.36695v1 Announce Type: new Abstract: Self-distillation turns knowledge distillation into a closed learning loop and offers a path toward recursive self-improvement. Without an external tea...

By Rui Wang, Ruijie Wang, Bo Chen, Jiangxuan Long, Yingyu Liang

llms efficiency

arXiv AI

Sep 30

Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving

arXiv:2609.36750v1 Announce Type: cross Abstract: Self-rewarding reinforcement learning (RL) enables large language models (LLMs) to self-evolve without human labels. Existing ensemble-based methods...

By Yiming Wang, Yikang Liu, Qingyuan Tian, Xingyu Chen, Zhuosheng Zhang, Zhaopeng Tu, Rui Wang

llms reinforcement-learning benchmarks

arXiv Machine Learning

Sep 30

Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisation

arXiv:2609.36788v1 Announce Type: new Abstract: Incorporating rich task-relevant context, such as domain knowledge and external observations, is a key capability yet remains challenging for Bayesian...

By Zhongwei Yu, Sourabh Roy, Bin Cao, Xue Yan, Anjie Liu, Jun Wang

llms rag benchmarks

arXiv Machine Learning

Sep 30

Towards Better Training Signal: Advantage Clipped Policy Optimization

arXiv:2609.36816v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a cornerstone for improving the reasoning capabilities of large language models (LLMs), but the need for on-poli...

By Ruichuan Huang, Jinghan Liu, Congliang Chen

llms reinforcement-learning benchmarks

arXiv Machine Learning

Sep 30

Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness

arXiv:2609.36945v1 Announce Type: new Abstract: We study the learning dynamics of fine-tuning a policy model on self-generated and reward-weighted data, with particular focus on a generalized version...

By Zhiwei Wang, Yanxi Chen, Yaliang Li, Bolin Ding

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Sep 30

Cool the Sampler, Not the Learner: Sampling Temperature Moves the Staleness Cliff of Importance-Corrected GRPO

arXiv:2609.36953v1 Announce Type: new Abstract: Production RL for language models lets the sampler fall behind the learner and repairs the resulting mismatch with a truncated importance weight. We as...

By Taiheng Pan

llms reinforcement-learning

arXiv Machine Learning

Sep 30

TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks

arXiv:2609.36968v1 Announce Type: new Abstract: Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples. While conventional methods...

By Doyun Choi, Dooho Lee, Jaemin Yoo

llms benchmarks

arXiv Machine Learning

Sep 30

High-Resolution Dynamic Functional Connectivity Generation with Graph-Variate Flow Matching

arXiv:2609.37037v1 Announce Type: new Abstract: High-resolution dynamic functional connectivity (DFC) can reveal rapidly evolving brain-network interactions, but short temporal windows yield noisy, o...

By Om Roy, Yashar Moshfeghi, Keith Malcolm Smith

llms diffusion

arXiv Machine Learning

Sep 30

Looped Transformers as Optimizers

arXiv:2609.37379v1 Announce Type: new Abstract: Looped Transformers provide a parameter-efficient approach to depth scaling by repeatedly applying shared Transformer blocks. Recent reasoning models h...

By Yulong Huang, Chen Jiang, Zhanpeng Zhou, Hongtao Zhang, Tianyu Li, Tianyu He, Xiangyu Zhang, Bojun Cheng

llms

arXiv Machine Learning

Sep 30

Probability Contracts: Accuracy, Coherence, and Decisions Across LLM Interfaces

arXiv:2609.37470v1 Announce Type: new Abstract: A probability used for a decision should refer to the same event across equivalent requests. We introduce probability contracts, a benchmark connecting...

By Han Chen, Yingrui Li

llms benchmarks

arXiv AI

Sep 30

Risk-Controlled Selective LLM Answering by Pricing Label-Free Checks

arXiv:2609.37493v1 Announce Type: cross Abstract: Serving an answer from a large language model requires deciding when to abstain, yet a verifier's ranking accuracy alone does not determine the error...

By Dongyub Jude Lee, Jungseob Lee, Chanjun Park, Hyeonseok Moon, Heuiseok Lim

llms

arXiv AI

Sep 30

Hierarchical Compression of Vision-Language Model Benchmarks

arXiv:2609.37515v1 Announce Type: cross Abstract: Thorough evaluation of vision-language models (VLMs) has become prohibitively expensive, as benchmarks span an ever-broader spectrum of capabilities...

By Hyunjong Ok, Seunggu Kang, Jaeho Lee

llms rag efficiency multimodal benchmarks

arXiv Machine Learning

Sep 30

Why Adaptive Optimizers Underestimate Rare Tokens

arXiv:2609.37535v1 Announce Type: new Abstract: In the softmax output layer, a rare token receives a small positive logit gradient on most steps and a much larger negative gradient on the few steps w...

By Sangsidhya Kar

llms rag safety

arXiv AI

Sep 30

ReLMem: Learning Recurrent Memory for Longitudinal EHR Modeling

arXiv:2609.37587v1 Announce Type: cross Abstract: Longitudinal electronic health record (EHR) modeling requires integrating new visits with an expanding patient history. Yet the continual accumulatio...

By Zijie Meng, Xiwei Dai, Yingying Zhang, Jian Wu, Xian Wu, Zuozhu Liu

llms

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