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ACL 2026×7ICLR 2026×3CVPR 2026×2AAAI 2026KDD 2026可解释性推理强化学习与后训练多模态检索与知识
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0 一作 · 14 篇 · ACL 2026 / ICLR 2026 / CVPR 2026 / AAAI 2026 / KDD 2026

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2026 顶会论文

14
作者位次 #6/7ACL 2026Poster· Natural Language Processing

Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation

Yuhao Wang · Ruiyang Ren · Yucheng Wang · Wayne Xin Zhao · Jing Liu · hua wu · Haifeng Wang

摘要

Long-form question answering (LFQA) requires open-ended long-form responses that synthesize coherent, factually grounded content from multi-source evidence. This makes reinforcement learning (RL) reward design critical. The reward must be verifiable for faithful grounding and stable optimization....

强化学习与后训练检索与知识推理OpenReview 项目页
作者位次 #7/8ACL 2026Poster· Natural Language Processing

Distributional Clarity: The Hidden Driver of RL -Friendliness in Large Language Models

Shaoning Sun · Mingzhu Cai · Huang He · Bingjin Chen · Siqi Bao · Yujiu Yang · hua wu · Haifeng Wang

摘要

Language model families exhibit striking disparity in their capacity to benefit from reinforcement learning: under identical training, models like Qwen achieve substantial gains, while others like Llama yield limited improvements. Complementing data-centric approaches, we reveal that this dispari...

推理可解释性强化学习与后训练OpenReview 项目页
作者位次 #10/11ACL 2026Poster· Natural Language Processing

Uncertainty-Aware Routing for Principled Alignment with M o E Dynamics

Yilong Chen · Junyuan Shang · Yuchen Feng · Zhenyu Zhang · Naibin Gu · Ziqi Wang · Tingwen Liu · Shuohuan Wang · YU SUN · hua wu · Haifeng Wang

摘要

Mixture-of-Experts (MoE) is a cornerstone for scaling LLMs, yet its training dynamics remain poorly understood, often leading to sub-optimal specialization. Moving beyond static routing, we present a systematic study of the MoE lifecycle using Helmholtz Free Energyand Router Entropy. We identify...

效率与系统对齐与偏好OpenReview 项目页
作者位次 #9/10ACL 2026Poster· Natural Language Processing

A ttn PO : Attention-Guided Process Supervision for Efficient Reasoning

Shuaiyi Nie · Dingsiyu · Wenyuan Zhang · Linhao Yu · Tianmeng Yang · Yao Chen · Weichong Yin · YU SUN · hua wu · Tingwen Liu

摘要

Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet often overthink, generating redundant reasoning without performance gains. Existing trajectory-level length penalties often fail to effectively short...

强化学习与后训练推理可解释性OpenReview 项目页
作者位次 #10/12ACL 2026Findings· Findings of ACL

Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping

Yao Chen · Yilong Chen · Yinqi Yang · Junyuan Shang · Zhenyu Zhang · Zefeng Zhang · Shuaiyi Nie · Shuohuan Wang · YU SUN · hua wu · Haifeng Wang · Tingwen Liu

摘要

Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution.Under this paradigm, the network structure remains static along the training timeline, and additional computational depth is uniformly ass...

可解释性效率与系统长上下文与架构OpenReview 项目页
作者位次 #11/12ACL 2026Findings· Findings of ACL

M o E Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free

Yishu Lei · Shuwei He · Hu Jing · Dan Zhang · Xianlong Luo · Danxiang Zhu · Shikun Feng · Rui Liu · Jingzhou HE · YU SUN · hua wu · Haifeng Wang

摘要

Extending the input modality of Large Language Models (LLMs) to the audio domain is essential for achieving comprehensive multimodal perception. However, it is well-known that acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental conte...

多模态效率与系统可解释性OpenReview 项目页
作者位次 #11/12ACL 2026Findings· Findings of ACL

CORD : Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation

Hu Jing · Danxiang Zhu · Xianlong Luo · Dan Zhang · Shuwei He · Yishu Lei · Shikun Feng · Hai-Tao Zheng · Jingzhou HE · YU SUN · hua wu · Haifeng Wang

摘要

Large Audio Language Models (LALMs) have garnered significant research interest. Despite being built upon text-based large language models (LLMs), LALMs frequently exhibit a degradation in knowledge and reasoning capabilities. We hypothesize that this limitation stems from the failure of current...

强化学习与后训练评测与基准检索与知识OpenReview 项目页

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