- AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling
Liang Ding · Mar 22, 2026 · Citations: 0
Demonstrations Human EvalLlm As Judge
LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…
- Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment
Jing Zhao, Ting Zhen, Junwei Bao, Hongfei Jiang, Yang Song · Feb 14, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability.
- When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation
Henry Peng Zou, Chunyu Miao, Wei-Chieh Huang, Yankai Chen, Yue Zhou · Apr 1, 2026 · Citations: 0
Critique Edit Simulation Env
As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revising goals, during mid-task execution…
- Go-Browse: Training Web Agents with Structured Exploration
Apurva Gandhi, Graham Neubig · Jun 4, 2025 · Citations: 0
Simulation Env
To address this, we propose Go-Browse, a method for automatically collecting diverse and realistic web agent data at scale through structured exploration of web environments.
- DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment
James Wedgwood, Aashiq Muhamed, Mona T. Diab, Virginia Smith · Mar 23, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Preference alignment is usually achieved by weight-updating training on preference data, which adds substantial alignment-stage compute and provides limited mechanistic visibility.
- GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA
Zhichao Wang · Oct 27, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
This paper proposes Group-relative Implicit Fine-Tuning (GIFT), a reinforcement learning framework for aligning large language models (LLMs) that unifies on-policy optimization with implicit preference learning.
- Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents
Haiyang Xu, Xi Zhang, Haowei Liu, Junyang Wang, Zhaozai Zhu · Feb 15, 2026 · Citations: 0
Simulation Env
The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features instruct/thinking variants in multiple sizes (2B/4B/8B/32B/235B) and supports a range of platforms (desktop, mobile, browser, and more) to enable cloud-edge…
- PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
Shangjian Yin, Shining Liang, Wenbiao Ding, Yuli Qian, Zhouxing Shi · Oct 8, 2025 · Citations: 0
Pairwise Preference
Despite its small size, fine-tuning Llama-3-8B-Base on PiKa-SFT even outperforms the official Llama-3-8B-Instruct model trained on over 10M proprietary examples on widely used benchmarks such as AlpacaEval 2.0 and Arena-Hard.
- Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty
Yao Xiao, Jung-jae Kim, Roy Ka-wei Lee, Lidong Bing · Oct 7, 2025 · Citations: 0
Pairwise Preference
Self-play preference optimization has emerged as a prominent paradigm for aligning large language models (LLMs).
- R-WoM: Retrieval-augmented World Model For Computer-use Agents
Kai Mei, Jiang Guo, Shuaichen Chang, Mingwen Dong, Dongkyu Lee · Oct 13, 2025 · Citations: 0
Simulation Env
Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments by simulating future states and predicting action outcomes, potentially eliminating costly trial-and-error exploration.
- TARo: Token-level Adaptive Routing for LLM Test-time Alignment
Arushi Rai, Qiang Zhang, Hanqing Zeng, Yunkai Zhang, Dipesh Tamboli · Mar 19, 2026 · Citations: 0
Pairwise Preference
Recent test-time alignment methods offer a lightweight alternative, but have been explored mainly for preference alignment rather than reasoning.
- Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization
Junming Yang, Ning Xu, Biao Liu, Shiqi Qiao, Xin Geng · Sep 27, 2025 · Citations: 0
Pairwise Preference
To bridge this gap, we propose Meta-Weighted Adaptive Preference Optimization (MetaAPO), a novel framework that dynamically couples data generation with model training.
- Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms
Zeguan Xiao, Yun Chen, Guanhua Chen, Ke Tang · Jun 11, 2025 · Citations: 0
Pairwise Preference
Direct Alignment Algorithms (DAAs), such as Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO), have emerged as efficient alternatives to Reinforcement Learning from Human Feedback (RLHF) algorithms for aligning…
- C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
Akira Kawabata, Saku Sugawara · Apr 15, 2026 · Citations: 0
- WebXSkill: Skill Learning for Autonomous Web Agents
Zhaoyang Wang, Qianhui Wu, Xuchao Zhang, Chaoyun Zhang, Wenlin Yao · Apr 14, 2026 · Citations: 0
- Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence
Niklas Herbster, Martin Zborowski, Alberto Tosato, Gauthier Gidel, Tommaso Tosato · Apr 9, 2026 · Citations: 0
- Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies
Zhanzhi Lou, Hui Chen, Yibo Li, Qian Wang, Bryan Hooi · Apr 1, 2026 · Citations: 0
- AdaRubric: Task-Adaptive Rubrics for LLM Agent Evaluation
Liang Ding · Mar 22, 2026 · Citations: 0
- Mediocrity is the key for LLM as a Judge Anchor Selection
Shachar Don-Yehiya, Asaf Yehudai, Leshem Choshen, Omri Abend · Mar 17, 2026 · Citations: 0
- AI Planning Framework for LLM-Based Web Agents
Orit Shahnovsky, Rotem Dror · Mar 13, 2026 · Citations: 0
- WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents
Yao Zhang, Shijie Tang, Zeyu Li, Zhen Han, Volker Tresp · Jan 29, 2026 · Citations: 0