Xuanren Chen, Chongyang Tao, Wenpeng Hu, Yong Wang, Bo Deng, Xinlei Zheng, Shuai Ma
Long-horizon tasks require large language model (LLM) agents to coordinate interdependent actions, a capability central to automating complex workflows. Stepwise group-based reinforcement learning provides finer credit than trajectory-level supervision, but reliable comparisons must account for differences in interaction history. Enforcing this consistency within the limited rollouts of each task can leave insufficient support for estimating action preferences. We propose Beyond-Local Policy Optimization (BLPO), which estimates task-progress advantages from experience shared across tasks and training updates. We summarize trajectory prefixes into task-progress states and abstract actions by their function, allowing corresponding decisions from different tasks to contribute to shared estimates. To control bias and noise from heterogeneous tasks, we accumulate context-relative returns in a global memory with balanced aggregation and reliability weighting. These global advantages complement local comparisons over normalized actions and trajectory-level credit without a learned critic or additional rollouts. Experiments on ALFWorld and WebShop with 1.5B and 7B policies show improved task success over the evaluated baselines under matched training budgets. Further analyses show faster learning and shorter trajectories with little computational overhead.
Xuanren Chen Beijing University of Aeronautics and Astronautics, Chongyang Tao Beihang University, Tao Shen Oracle, Shuai Ma Beihang University
Patient–trial retrieval is a challenging problem that requires nuanced clinical reasoning beyond surface-level semantic similarity. However, scarce and costly relevance annotations force existing approaches to rely on very limited supervision or zero-shot transfer, reducing the task to generic semantic matching and failing to capture multi-factor eligibility reasoning. To this end, we propose FACTrial, a factorized contrastive training framework that leverages LLMs to synthesize diagnosis-aware supervision for scalable patient–trial retrieval. FACTrial decomposes each patient note into a primary diagnosis and a set of concomitant, eligibility-triggering conditions, and constructs complementary contrastive signals through structured trial augmentation. Specifically, we generate primary-target and concomitant-target positives, together with clinically confusable near-miss negatives, to enforce diagnostic specificity under contrastive learning. Two specialized bi-encoder experts are trained to balance primary-diagnosis prioritization and concomitant-driven recall, and fused into a single deployable retriever. Experiments on three public benchmarks demonstrate that FACTrial achieves state-of-the-art performance, improving both top-ranked quality and high-recall coverage.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表