arXiv AI October 15, 2026 By Zhentao Xu

PID-Gated Continual Prompt Adaptation Under Noisy Feedback

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arXiv Machine Learning

Jun 11

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

By Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal, Shashwat Saxena, Jesse Zhang, Giri Anantharaman, Cleah Winston, Chaoyi Pan, Douglas Chen, Nai-Chieh Huang, Zeynep Temel, Oliver Kroemer, Sergey Levine, Abhishek Gupta, Hongkai Dai, Paarth Shah, Max Simchowitz

diffusion reinforcement-learning robotics fine-tuning benchmarks

arXiv Computer Vision

Oct 27

Test-Time Prompt-Agnostic Decomposition

arXiv:2610.35090v1 Announce Type: new Abstract: Test-Time Prompt Tuning (TPT) adapts large pretrained models to unseen target distribution shifts by updating only lightweight prompt tokens. Existing...

By Junze Wang, Lei Fan, Dezheng Zhang, Donglin Di, Yang Song, Sidong Liu, Cong Cong

arXiv Computation and Language

Oct 15

Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems

arXiv:2505.13546v2 Announce Type: replace-cross Abstract: Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Exist...

By Ke Chen, Xucheng Yu, Yufei Zhou, Haohan Wang

llms agents safety

arXiv Machine Learning

Oct 23

GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer

GFlowPO introduces a probabilistic framework for optimizing language model prompts by treating prompt search as posterior inference over latent prompts, regularized by a meta-prompted reference-LM prior. The method first fine‑tunes a lightweight prompt‑LM using an off‑policy Generative Flow Network objective that reuses past prompt evaluations for sample‑efficient exploration. It then applies a training‑free Dynamic Memory Update to refine the meta‑prompt with diverse and top‑performing prompts, leading to consistent improvements over recent discrete prompt optimization baselines on few‑shot classification, instruction induction, and question‑answering tasks.

By Junmo Cho, Suhan Kim, Sangjune An, Minsu Kim, Dong Bok Lee, Heejun Lee, Sung Ju Hwang, Hae Beom Lee

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Oct 9

A Score Is Not a Policy: Measuring the Value of Adaptive Revision

The paper investigates how a higher‑level controller in a hierarchical latent reasoner decides whether to preserve or replace a lower‑level strategy. Experiments across different training seeds show that learned revision timing can produce varied policies, but none surpasses a fixed, forced timing policy on the same checkpoint. The study highlights that state‑dependent control can alter actions without improving task performance, and that timing decisions are order‑sensitive and largely captured by a strong fixed schedule.

By Ayushi Chadha

agents

arXiv Computation and Language

Oct 1

AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization

arXiv:2609.39927v1 Announce Type: new Abstract: Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompt...

By Junyang Chen, Zecheng Wang, Jingbang Chen

benchmarks