FGSS-BO introduces a Feedback‑Guided Search State (FGSS) framework that structures hypothesis management for large language model (LLM)‑guided Bayesian optimization. The system couples an LLM reflector, which proposes and revises hypotheses, with a deterministic curator that validates them and tracks test records, thereby linking language‑based guidance to observable evidence and parameter interventions. Applied to the LLAMBO platform, FGSS‑BO improves sample‑mean final best and search AUC on two retrospective protocol/media tasks, while also enabling inspection of hypotheses and their revisions across search rounds.
By Hao Chen, Ziyang Ling, Ronald X. Xu, Mingzhai Sun
The paper introduces LM2LM, a method that trains large language models (LLMs) to propose improvements for other LLMs using reinforcement learning (RL). It employs a frozen research world model (RWM) to predict the gains of proposals, providing a cheaper surrogate reward, and periodically validates high‑scoring proposals to keep feedback reliable. Experiments across eight LM development environments show that LM2LM enables a Qwen3.8‑27B model to outperform GPT‑6‑sol on held‑out tasks while reducing data‑generation cost by 33%, and it generalizes to unseen model families.
By Minhua Lin, Zijun Wang, Zewen Liu, Zhaotian Weng, Bing He, Zhan Shi, Benoit Dumoulin, Xiang Zhang, Suhang Wang, Hanqing Lu
arXiv:2604. 00830v3 Announce Type: replace-cross Abstract: Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at inference time.
By Zhanzhi Lou, Hui Chen, Yibo Li, Qian Wang, Bryan Hooi
arXiv:2610.18572v1 Announce Type: new
Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically constructi...
By Ruohong Liu, Bohan Tang, Lekang Jiang, Jack Umenberger, Yiwen Guo
OptiCom introduces a unified framework for state-conditioned composition in large language model (LLM)-driven optimization. It models LLM optimizers within a shared configuration space (artifact, query, operator, evaluation, memory, strategy) and uses an Optimization Controller to dynamically compose mechanisms while a Strategy Adapter refines long-term preferences. Experiments on 32 benchmark groups show OptiCom outperforms 14 configurations, achieving the top score in 23 groups.
By Chenxing Wei, Sichen Liu, Lizhao Liu, Ningyuan Sun, Chen Bingzhou, Ying He, Bo Jiang, Fei Yu, Yao Shu
arXiv:2606. 24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferring what to answer rather than how to reason.
By Tianyuan Shi, Canbin Huang, Bei Li, Xin Chen, Xiaojun Quan, Jingang Wang, Qifan Wang