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
We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round $t \in [T]$, both player and environment observe...
The paper investigates how Large Language Models (LLMs) handle the exploration–exploitation trade‑off in Bayesian Optimization, noting that single‑agent LLM approaches suffer from cognitive overload and unstable search dynamics. It proposes a multi‑agent framework that separates strategic policy mediation from tactical candidate generation, allowing explicit, observable, and adjustable exploration–exploitation decisions. Experiments on continuous optimization benchmarks show that this decomposition improves LLM‑mediated search effectiveness.
By Mohammadsina Almasi, Andrea Carbonati, Hadis Anahideh
arXiv:2610.19804v1 Announce Type: new
Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round...
By Kapilan Balagopalan, Hao Qin, Kwang-Sung Jun, Chicheng Zhang
arXiv:2604. 17244v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents for sequential decision-making struggle to produce diverse outputs.
By Priya Gurjar, Md Farhan Ishmam, Kenneth Marino
CB-Orchestrator is an adaptive framework for optimizing large language model (LLM) agent workflows. It separates workflow generation—via evolutionary search—from workflow selection, which is treated as a contextual bandit problem to enable sample‑efficient learning. The approach reduces training‑phase API token usage by up to 40.55% and overall token overhead by at least 29.73%, while outperforming baselines on five benchmarks, including the MATH dataset.
By Jiahang Sun, Zhiwei Shang, Zhipiao Liu, Hongwei Yang, Guoqing Xie, Shuang Qiu, Zhongxiang Dai