The paper introduces GIST, a Gibbs‑Targeted Hierarchical Data Selection method for offline reinforcement learning with verifiable rewards (RLVR). By analyzing the bidirectional KL divergence between a policy and its Gibbs‑optimal target, GIST selectively supervises correct responses on‑policy and incorrect responses near the policy’s mode, while focusing token supervision on mismatched tokens. Experiments on GSM8K, MATH‑500, and MBPP show that GIST improves average accuracy across three models and outperforms seven baselines on Qwen3‑0.6B/GSM8K, all while supervising 66.6% fewer tokens.
By Xuliang Huang, Tianxiang Zhao, Chengwei Qin, Yao Shu
arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.
By Jan Malte T\"opperwien, Aditya Mohan, Marius Lindauer
The paper introduces a denoised counterfactual protocol to accurately measure the selectable headroom of world action models (WAMs) and uses these measurements to build a fail‑gated selector. Across four WAMs and two benchmarks, the study shows that the standard estimate overstates selectable headroom, that structured action perturbations can add up to 16 points of headroom, and that imagined futures retain candidate‑outcome information without loss. The selector improves closed‑loop task success from 76.9 % to 81.6 % on held‑out training episodes and from 62.6 % to 69.8 % on unseen tasks.
By Haojia Gao, Yue Cao, Yumin Liu, Haohua Que, Mingkai Liu, Guanqiu Guo, Zhuo Zou, Bokui Chen, Xueqian Wang
arXiv:2605. 17877v2 Announce Type: replace Abstract: A significant hurdle for current LLMs is the execution of complex, multi-stage tasks.
By Wonjoong Kim, Yeonjun In, Sangwu Park, Dongha Lee, Chanyoung Park
arXiv:2607. 27422v2 Announce Type: replace Abstract: Goal-conditioned action generators can produce many plausible actions.
By Ayushman Singh, Siddharth Aphale
arXiv:2607. 29606v1 Announce Type: cross Abstract: A fundamental challenge of vector search is achieving consistently high recall while minimizing computational costs.
By Hakan Ferhatosmanoglu, Kushal Kumar, Tal Wagner, Andy Warfield
arXiv:2608. 12847v1 Announce Type: new Abstract: Retrieval can identify a past trajectory that may matter, yet it does not specify how an acting agent should use that trajectory after users, entities, constraints, or environment state have changed.
By Yifei Li, Heng Wang, Lingling Zhang, Muye Huang, Xinyu Zhang, Jiashuai Liu, Hang Yan, Rongman Xu
Search-G1 is a reward framework that encourages language agents to retrieve external information only when necessary and to ground their answers in that evidence. It uses two hidden‑state readouts—one predicting whether a closed‑book answer suffices and another measuring sensitivity to evidence deletion—to calibrate retrieval necessity and evidence reliance. The reward system gates evidence credit by necessity, rewards sufficient direct answers, and penalizes redundant searches, all without requiring costly annotations or LLM‑judge inference.
By Ruoxi Cheng, Haoxuan Ma, Hongyi Zhang, Junming Zhang, Ranjie Duan, Qiaolin Xia, Hao Wang, Yu Lu, Haibo Shi, Xingjun Ma
The paper introduces State‑Conditioned Minimal Sufficient Evidence Recovery (SER), a method that, given a coding agent’s current state, reconstructs a compact set of evidence passages that collectively provide all facts needed for the agent’s next decision. Using the SERBench dataset of 500 held‑out states from 45 repositories, the authors show that their MSS‑Complement approach recovers a complete evidence set for 73.0 % of states with five items and 80.6 % with eight, outperforming baseline ranking methods. The study also demonstrates that this set‑level policy improves downstream performance on AMA‑Bench and highlights the importance of retrieving missing facts rather than merely re‑ranking similar passages.
By Zhexi Feng, Ruiyi Zhang, Yongbo Yang, Pengtao Xie
arXiv:2610.28556v1 Announce Type: cross
Abstract: Large language models (LLMs) can leverage semantic feature names alongside summary statistics for tabular feature selection, yet it remains unclear w...
By Fateme Memar, Dongjie Wang, Arvin Agah
The paper introduces Signals and Queries (S&Q), a lightweight first-stage relevance filter for LLM agents that removes at least 30% of candidate policies before downstream evaluation while maintaining high recall. S&Q uses offline-generated activating examples and hard negatives to train a logistic classifier that predicts policy relevance via embeddings and vector dot products, eliminating per-turn LLM calls. Evaluated on 9,144 relevance labels across 254 conversation prefixes and 36 airline policies, S&Q outperforms top‑K and cosine‑threshold retrieval in precision at every recall level, consistently across two embedding providers.
By Yam Marcovitz
The paper investigates how personalized agents decide to use, ignore, update, or query retrieved user memory before acting on a task. An empirical audit protocol is developed to test structured intermediate outputs, revealing that while exposing state definitions improves accuracy, an explicit state-output field does not significantly enhance policy accuracy for large language models. The study also shows that example-level accuracy overstates consistency, with full four‑way family success being rare, and that providing benchmark‑associated state labels merely conditions predictions rather than proving internal fidelity.
By Yihang Chen, Pin Qian, Su Wang, Chong Peng, Huan Xu, Shuaiting Li, Yiqi Sun