6 papers
Recovering Policy-Induced Errors: Benchmarking and Trajectory Synthesis for Robust GUI Agents
Tianpeng Bu, Xin Liu, Qihua Chen +7
While GUI agents have advanced rapidly, they often lack the robustness to recover from their own errors, hindering real-world deployment. To bridge this gap at both the evaluation…
Reasoning-preserved Efficient Distillation of Large Language Models via Activation-aware Initialization
Junlin He, Yihong Tang, Tong Nie +5
Efficient Distillation (EDistill) compresses large language models (LLMs) by structured pruning parameters and tuning lightweight modules with high training efficiency. Although th…
Long Live The Balance: Information Bottleneck Driven Tree-based Policy Optimization
Hao Jiang, Shurui Li, Tianpeng Bu +7
Recent advances in online reinforcement learning (RL) for large language models (LLMs) have demonstrated promising performance in complex reasoning tasks. However, they often exhib…
A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models
Mario Döbler, Robert A. Marsden, Tobias Raichle +1
In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models…
Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation
Mario Döbler, Florian Marencke, Robert A. Marsden +1
Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update…
Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
Martin Wimpff, Mario Döbler, Bin Yang
Providing a promising pathway to link the human brain with external devices, Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding capabilities, primarily dri…