most citedReinforcement World Model Learning for LLM-based Agents

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cs.LG2026

TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents

Leitian Tao, Baolin Peng, Wenlin Yao +5

Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-…

cs.LG2026

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents

Rui Yang, Qianhui Wu, Yuxi Chen +7

Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the stronges…

cs.LG2026

Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior

Zeyi Huang, Xuehai He, LiLiang Ren +8

We study Latent Recurrent Transformer (LRT), a lightweight augmentation of autoregressive transformers that reuses a high-level source-layer hidden state from the previous token as…

cs.LG2026

GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

Rui Yang, Qianhui Wu, Zhaoyang Wang +8

Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks. This gap stems from two limitations: a shortage of high-quality, action-align…

cs.LG2025

ThetaEvolve: Test-time Learning on Open Problems

Yiping Wang, Shao-Rong Su, Zhiyuan Zeng +13

Recent advances in large language models (LLMs) have enabled breakthroughs in mathematical discovery, exemplified by AlphaEvolve, a closed-source system that evolves programs to im…